assessment of the individual auditory perception via evoked … · 2013-09-03 · assessment of the...

Assessment of the Individual

Auditory Perception

via Evoked Potentials

Der Technischen Fakultat derUniversitat Erlangen-Nurnberg

zur Erlangung des Grades

DOKTOR-INGENIEUR

vorgelegt vonDipl.-Ing. Martin Burger

Erlangen - 2008

Als Dissertation genehmigt vonder Technischen Fakultat der

Universitat Erlangen-Nurnberg

Tag der Einreichung : 11.10.2007Tag der Promotion : 13.12.2007Dekan : Prof. Dr. J. HuberBerichterstatter : Prof. Dr. Dr. U. Eysholdt

Prof. Dr. W. Kellermann

Bewertung der individuellen

auditiven Wahrnehmung

mittels akustisch evozierter

Potentiale

Der Technischen Fakultat derUniversitat Erlangen-Nurnberg

zur Erlangung des Grades

DOKTOR-INGENIEUR

vorgelegt vonDipl.-Ing. Martin Burger

Erlangen - 2008

Danksagung

Meinem Doktorvater Prof. Dr. Dr. Ulrich Eysholdt danke ich fur die Auf-nahme in seiner Abteilung und fur die Uberlassung des Themas. SeinEngagement ermoglichte interdisziplinare Strukturen, welche eine substan-tielle Basis meiner Arbeit waren.

Bei Herrn Prof. Dr. Walter Kellermann mochte ich mich fur die Ubernahmedes Korreferats bedanken.

Ein großer Dank geht an meine beiden Betreuer PD Dr. Michael Dollingerund PD Dr. Jorg Lohscheller, deren unermudlicher Einsatz mir ein gutesBeispiel gab.

Ebenso danke ich Prof. Dr. Dr. Ulrich Hoppe fur die intensive Zusam-menarbeit und die vielen wertvollen Anregungen und Prof. Dr. Peter Kum-mer fur die Erorterung der medizinischen Fragestellungen.

Herr Dr. Raphael Schwarz und Herr Dipl.-Ing. Tobias Wurzbacher habenden Entstehungsprozess der vorliegenden Arbeit unmittelbar miterlebt undkonstruktiv unterstutzt. Fur die zahlreichen Diskussionen und Anregungenmochte ich mich herzlich bedanken.

Ein ganz besonderer Dank gilt meiner Familie, die meinen bisherigen Le-bensweg so liebevoll unterstutzt hat.

Die vorliegende Arbeit wurde im Rahmen des Projekts DFG Ey15/7 derDeutschen Forschungsgemeinschaft und durch die Erlanger leistungsbezo-gene Anschubfinanzierung und Nachwuchsforderung (ELAN PP-05.09.26.1)gefordert.

Zusammenfassung

Fur die sprachliche Kommunikation als soziale Schlusselkompetenz benotigtder Mensch eine Sende- und eine Empfangsmoglichkeit. Im Gehor als Emp-fangsteil finden nach aktueller Auffassung zwei Verarbeitungsstrategienparallel statt: in einem ”bottom-up” genannten Signalverarbeitungsprozesswerden die physikalischen Schallereignisse in neuronale Impulse umgesetztund Merkmale extrahiert; in einem gleichzeitig verlaufenden ”top-down”-Prozess werden die Merkmale mit Erwartungen und Erfahrungen abgegli-chen, die in hoheren Zentren vorliegen. Die Erfahrungen werden im fruhenKindesalter erworben. Fehlfunktionen der Lernphase in diesem Lebensalter— verursacht durch eine Storung der auditiven Wahrnehmung — konnengravierende Folgen zunachst fur die Sprachentwicklung, langfristig fur dieSprachkompetenz des Menschen haben.

Ziel der vorliegenden Arbeit ist es, eine objektive Diagnostik auditiverWahrnehmungsstorungen zu ermoglichen, die sowohl unabhangig von derKooperation des Patienten als auch von der Interpretation des Untersu-chers ist. Als geeignetes Verfahren erweist sich die Messung akustischevozierter Potentiale. Dort wird wahrend akustischer Stimulation ein EEGaufgezeichnet, in dem sich die physiologischen Wahrnehmungsprozesse inder neuronalen Antwort des Gehirns auf den akustischen Stimulus ab-bilden. In der vorliegenden Arbeit werden Verfahren zur objektiven Mes-sung von auditiven Diskriminationsleistungen und zur Bestimmung deszeitlichen Auflosungsvermogens der auditiven Verarbeitung vorgestellt.Weiterhin werden die maßgeblichen Mechanismen der prakognitiven Sprach-verarbeitung erfasst und gleichzeitig ein Ansatz fur deren klinische Nutz-barmachung vorgeschlagen. Die geleisteten Beitrage zeigen, dass akustischevozierte Potentiale vorzuglich fur die Modellierung auditiver Wahrneh-mungsprozesse geeignet sind. Ob sie die fur die audiologische Diagnostikgeforderte Trennscharfe erreichen, muss in weiteren Arbeiten untersuchtwerden.

Contents

1 Introduction 1

2 Fundamentals 9

2.1 Auditory Perception . . . . . . . . . . . . . . . . . . . . . 92.1.1 Auditory Pathway . . . . . . . . . . . . . . . . . . 92.1.2 Auditory Functions . . . . . . . . . . . . . . . . . . 112.1.3 Psychological Model Representation . . . . . . . . . 13

2.2 Auditory Evoked Potentials . . . . . . . . . . . . . . . . . 152.2.1 Principles . . . . . . . . . . . . . . . . . . . . . . . 152.2.2 Neural Generators . . . . . . . . . . . . . . . . . . 182.2.3 Cortical AEP Recordings . . . . . . . . . . . . . . . 202.2.4 Cortical AEP Components . . . . . . . . . . . . . . 21

3 Evoked Potentials in Children with CAPD 25

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 253.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.2.1 Participants . . . . . . . . . . . . . . . . . . . . . . 273.2.2 Stimuli and Procedure . . . . . . . . . . . . . . . . 293.2.3 EEG Recording . . . . . . . . . . . . . . . . . . . . 303.2.4 SNR Determination . . . . . . . . . . . . . . . . . . 313.2.5 Wavelet Transform . . . . . . . . . . . . . . . . . . 313.2.6 H1-Method . . . . . . . . . . . . . . . . . . . . . . 35

3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.3.1 Cortical Stimulus Responses . . . . . . . . . . . . . 393.3.2 MMN . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 483.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4 Gap Detection 53

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 53

i

ii Contents

4.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554.2.1 Participants . . . . . . . . . . . . . . . . . . . . . . 554.2.2 Subjective Test: Gap Detection Procedure . . . . . 554.2.3 Objective Test: AEP Measurement . . . . . . . . . 56

4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594.3.1 Individual Gap Detection Threshold . . . . . . . . 594.3.2 AEP Characteristics . . . . . . . . . . . . . . . . . 604.3.3 Correlation: Threshold—AEP . . . . . . . . . . . . 64

4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5 Decomposition of Speech-Evoked Potentials 69

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 695.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

5.2.1 Participants . . . . . . . . . . . . . . . . . . . . . . 725.2.2 Stimuli and Procedure . . . . . . . . . . . . . . . . 725.2.3 EEG Recording . . . . . . . . . . . . . . . . . . . . 745.2.4 AEP Peak Detection . . . . . . . . . . . . . . . . . 755.2.5 Comparison of the Evoked Potentials . . . . . . . . 755.2.6 Synthetic Waveform Optimization . . . . . . . . . . 76

5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795.3.1 Cortical Responses . . . . . . . . . . . . . . . . . . 795.3.2 Speech–Noise Pairs . . . . . . . . . . . . . . . . . . 835.3.3 Fitting Results . . . . . . . . . . . . . . . . . . . . 87

5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 915.4.1 Speech- and Non-Speech-Evoked Potentials . . . . . 915.4.2 Hemispheric Asymmetries . . . . . . . . . . . . . . 915.4.3 Speech–Noise Pairs . . . . . . . . . . . . . . . . . . 925.4.4 Fitting of the Speech-Evoked Potentials . . . . . . . 935.4.5 Weighting Parameters of the Fitting . . . . . . . . 945.4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . 94

6 Summary and Outlook 95

List of Abbreviations 103

Bibliography 105

Contents iii

List of Figures 119

List of Tables 121

Curriculum Vitae 123

iv Contents

Inhaltsverzeichnis

1 Einleitung 5

2 Grundlagen 9

2.1 Auditive Wahrnehmung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.1 Die Horbahn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.2 Auditive Teilleistungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.1.3 Psychologische Modellvorstellung . . . . . . . . . . . . . . . . . . . . . . 13

2.2 Akustisch evozierte Potentiale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.1 Grundlagen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.2 Neuronale Generatoren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.2.3 Aufnahme kortikaler AEP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.2.4 Komponenten kortikaler AEP . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3 AEP bei Kindern mit AVWS 25

3.1 Einleitung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.2 Methoden . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.2.1 Teilnehmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.2.2 Stimuli und Durchfuhrung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.2.3 EEG-Aufzeichnung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.2.4 SNR-Bestimmung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.5 Wavelet-Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.6 H1-Methode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.3 Ergebnisse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.3.1 Kortikale Stimulus-Antworten . . . . . . . . . . . . . . . . . . . . . . . . . . 393.3.2 MMN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.4 Diskussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

i

ii Inhaltsverzeichnis

3.5 Schlussfolgerungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4 Luckenerkennung 53


4.2.1 Teilnehmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554.2.2 Subjektiver Test: Luckenerkennung . . . . . . . . . . . . . . . . . . . . 554.2.3 Objektiver Test: AEP-Messungen . . . . . . . . . . . . . . . . . . . . . . 56

4.3 Ergebnisse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594.3.1 Schwelle der Luckenerkennung . . . . . . . . . . . . . . . . . . . . . . . . . 594.3.2 AEP-Charakteristika . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.3.3 Korrelation: Schwelle—AEP . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.4 Diskussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.5 Schlussfolgerungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5 Zerlegung sprachevozierter Potentiale 69


5.2.1 Teilnehmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725.2.2 Stimuli und Durchfuhrung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725.2.3 EEG-Aufzeichnung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745.2.4 AEP-Peak-Erkennung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755.2.5 Vergleich der evozierten Potentiale . . . . . . . . . . . . . . . . . . . . . 755.2.6 Optimierung mit synthetischen Kurven . . . . . . . . . . . . . . . . 76

5.3 Ergebnisse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795.3.1 Kortikale Antworten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795.3.2 Sprach-Rauschpaare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 835.3.3 Anpassungsergebnisse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.4 Diskussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 915.4.1 Sprach- und Rausch-evozierte Potentiale . . . . . . . . . . . . . . . 915.4.2 Hemispharische Asymmetrien . . . . . . . . . . . . . . . . . . . . . . . . . . 915.4.3 Sprach-Rauschpaare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 925.4.4 Anpassung der sprachevozierten Potentiale . . . . . . . . . . . . . 935.4.5 Gewichtungsfaktoren der Anpassung . . . . . . . . . . . . . . . . . . . 945.4.6 Zusammenfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

6 Zusammenfassung und Ausblick 99

Inhaltsverzeichnis iii

Abkurzungsverzeichnis 103

Literaturverzeichnis 105

Abbildungsverzeichnis 117

Tabellenverzeichnis 119

Lebenslauf 121

iv Inhaltsverzeichnis

1 Introduction

Language is the most important way of human communication. It hassubstantial influence on the quality of life and on the social situation whichalready becomes apparent in the childhood. The best example are thedisadvantageous results of German school children with the PISA studywhich is interpreted as expression of weak language competence [1].

The prevalence of children with language disorders needing therapy amountsto 10% according to an estimation of the ”Deutsche Gesellschaft fur Pho-niatrie und Padaudiologie” [2]. A developmental language disorder is noconsistent disease pattern, but a complex retardation syndrome related tovarious processing layers. Possible reasons on receptive as well as on ex-pressive layers may be situated in non-lingual fields, like general mentalretardation [3] or sensory handicap [4]. In 3–6% of the cases the reasonsare assumed to be language-specific [5], and thus referred to as specificlanguage impairment (SLI). The elementary components of language pro-cessing develop in children of normal intelligence commonly till preschoolage [6]. Hence, early discovery of the individual SLI etiology is crucial foreffective intervention.

In the case of a receptive SLI, i.e. if the perception of speech is disordered,an intensive assessment of the hearing is necessary. The ears transformacoustic sounds in neural pulses leading to the brain. This process isreferred to as peripheral hearing. In the central hearing, the brain extractsthe sound features and compares them to expectations and experiencesresulting in a mental representation.

In clinical practice ears can be examined with modern objective methodswithout requiring the cooperation of the patient. Hence, the functioning ofthe peripheral hearing as potential cause of SLI can be diagnosed alreadyin the early childhood. In contrast, the examination of the central hearingis considerably more complicated. Traditionally, psychometric audiologicaloriented language tests are applied. Those tests suffer from testing always

1

2 1 Introduction

a combination of audition and cognitive capacities. Additionally, atten-tion and motivation of the patient are required for successful diagnostics.Regarding the relevant age, only experienced audiologists can subjectivelydraw conclusions from the test results about the state of audition. Theinsufficiencies of those tests are possibly responsible for a still controversialdiscussion about the incidence of impaired auditory functions among SLIchildren [7].

The aim of the current work is to objectify the assessment of the audi-tory perception of individuals. Objective test methods will allow directedand differentiated diagnostics of central hearing functions. For the re-alization auditory evoked potentials (AEPs) were chosen, which registerelectrophysiological correlatives of auditory activities via EEG. Today, theexamination of the auditory perception by means of AEP is on the cuspfrom neuro-scientific basic research to the application on clinical problems.

The major advantage of AEP compared to imaging techniques is the hightemporal resolution cortical processes can be observed with. This is es-sential for the analysis of auditory processes, since acoustic signals — incontrast to e.g. pictures — are transient and require cerebral real-timeprocessing.

In Chapter 2 of the present work anatomy and physiology of the humanhearing and its partitioning into distinct interacting auditory functions aresummarized. Additionally, principles of AEP recordings and commonlyused AEP measurement designs are explained. An overview of the charac-teristic components AEPs are composed of is given. The emphasis of thisconsideration is placed on the components relevant for this work.

The present work consists of three studies (arranged in Chapters 3 to 5)motivated by distinct clinical questions concerning the objective assessmentof auditory perception. In Chapter 3 a cross-sectional study is described inwhich the auditory performances of children suffering from central auditoryprocessing disorder (CAPD) are measured and compared to those of age-matched healthy control children. As electrophysiological approach themismatch negativity (MMN) is applied. The MMN is a component of theAEP which is supposed to reflect auditory discrimination abilities. Besidesthe treatment of audiological issues, methods for the fully automatic AEPanalysis are introduced and discussed. These methods base on statisticalapproaches and time-frequency analysis.

3

Speech intelligibility requires an adequate velocity in the processing of theacoustic input. Hence, the objective measurability of the individual audi-tory processing speed is addressed in Chapter 4. In this study, the temporalresolution of the central hearing is examined in normal hearing adults bothpsychometrically and via AEPs. As pointed out the responses of the brainto silent gaps in continuous stimuli provide valuable indications of the func-tionality of temporal auditory aspects. Parameters derived from the AEPmeasurements seem to reflect the same brain activity as the psychometrictest results.

For neurophysiological purposes, the determination of the cognitive level atwhich speech-specific processes initially occur is of special interest. Hence,the processing of meaningful speech is examined in Chapter 5. An AEPparadigm is introduced revealing the responses of the brain to speech withrespect to the phonetic stimulus features. The spatial distribution of thebrain responses indicates that speech-specific neural generators are acti-vated already on pre-attentive level. However, the characteristic of thebrain activity appears to be predominated by the phonetic composition ofthe stimulus. Encouraged by these results, an emulation of speech-evokedAEPs out of event-related basic modules is proposed. Clarifying the mech-anisms of AEP composition is an important issue in neurophysiology. Thiscould allow the decoupling of auditory processing from processes which arespecific to speech. The high concordance of the emulation results with thespeech-evoked AEPs confirms our assumptions.

4 1 Introduction

1 Einleitung

Sprache ist die wichtigste Art der menschlichen Kommunikation. Wiesich bereits im Kindesalter abzeichnet, beeinflusst das Sprachvermogen aufgrundlegende Weise die Lebensqualitat und soziale Stellung. Das besteBeispiel gibt das schlechte Abschneiden Deutscher Schulkinder bei derPISA-Studie, das vorrangig als Ausdruck schwacher Sprachkompetenz ge-wertet wird [1].

Die Pravalenz der Kinder mit einer behandlungsbedurftigen Sprachent-wicklungsstorung betragt 10% gemaß einer Schatzung der ”Deutsche Ge-sellschaft fur Phoniatrie und Padaudiologie” [2]. Eine kindliche Sprachent-wicklungsstorung ist kein einheitliches Krankheitsbild, sondern ein kom-plexes und vielschichtiges Retardierungssyndrom. Mogliche Ursachen so-wohl auf rezeptiver als auch auf expressiver Ebene konnen in nicht-sprach-lichen Bereichen liegen, wie etwa bei einer verzogerten Allgemeinentwick-lung [3] oder einer sensorischen Behinderung [4]. In 3–6% der Falle giltdie Ursache als sprachspezifisch, weshalb von einer spezifischen Sprachent-wicklungsstorung (SSES) gesprochen wird.

Gemeinhin entwickeln sich die grundsatzlichen Elemente der Sprachver-arbeitung bei Kindern normaler Intelligenz bereits im Vorschulalter [6].Aus diesem Grunde ist die fruhzeitige Erkennung der individuellen SSES-Atiologie entscheidend fur ein effizientes Einschreiten. Im Falle einer rezep-tiven SSES, d.h., wenn die Sprachwahrnehmung gestort ist, muss eine um-fangreiche Hordiagnostik erfolgen. Die Ohren setzen akustische Klange inneuronale Impulse um, die zum Gehirn fuhren. Dieser Prozess wird alsperipheres Horen bezeichnet. Im zentralen Gehor extrahiert das Gehirndie Klangmerkmale und vergleicht sie mit Erwartungen und Erfahrungen,was zu einer mentalen Reprasentation des akustischen Reizes fuhrt.

5

6 1 Einleitung

Heutzutage werden die Ohren in der klinischen Praxis mit objektiven Ver-fahren untersucht, ohne auf die Mitarbeit des Patienten angewiesen zusein. Somit kann die Funktionstuchtigkeit des peripheren Gehors als po-tentielle Ursache einer verzogerten Sprachentwicklung bereits in fruherKindheit getestet werden. Im Gegensatz dazu ist die Diagnostik des zen-tralen Gehors ungleich schwieriger. Traditionell werden hierfur audio-logisch orientierte Sprachtests verwendet. Diese Tests haben allerdingsden Nachteil, dass sie immer eine Kombination aus Gehor und kognitivenFahigkeiten messen. Daruber hinaus sind sie fur ein gultiges Ergebnisauf motivierte und aufmerksame Patienten angewiesen. Hinsichtlich derrelevanten Altersgruppe konnen ausschließlich erfahrene Audiologen sub-jektive Schlussfolgerungen uber den Zustand des gepruften Gehors ziehen.Moglicherweise sind die Unzulanglichkeiten dieser Tests ein Grund dafur,warum die Verbreitung beeintrachtigter auditiver Teilleistungen unter densprachentwicklungsgestorten Kindern immer noch nicht exakt bestimmtwerden kann [7].

Ziel der vorliegenden Arbeit ist es, die Einschatzung der individuellen au-ditiven Wahrnehmung zu objektivieren. Objektive Testmethoden ermog-lichen eine gezielte und differenzierte Diagnostik zentraler Horfunktionen.Fur deren Realisierung wurden die akustisch evozierten Potentiale (AEP)gewahlt, die elektrophysiologische Korrelate auditiver Gehirnaktivitat mit-tels EEG registrieren. Heute befindet sich die Untersuchung der auditivenWahrnehmung mittels AEP am Ubergang von der neurophysiologischenGrundlagenforschung hin zur Bearbeitung klinischer Fragestellung.

Der Hauptvorteil der AEP im Gegensatz zu bildgebenden Verfahren istdie hohe zeitliche Auflosung, mit der kortikale Prozesse beobachtet werdenkonnen. Dies ist entscheidend fur die Analyse auditiver Verarbeitungs-schritte, da akustische Signale — im Gegensatz zu z.B. Bildern — nurvorubergehend bestehen und somit zerebrale Echtzeit-Verarbeitung erfor-dern.

In Kapitel 2 dieser Arbeit wird Anatomie und Physiologie des menschlichenGehors und seine Enteilung in unterschiedliche interagierende auditive Teil-funktionen behandelt. Außerdem werden die Prinzipien der AEP-Aufzeich-nung und die ublichen AEP-Designs erklart. Ein Uberblick uber AEP-Charakteristika wird gegeben, wobei die Betonung auf den fur diese Arbeitrelevanten Komponenten liegt.

1 Einleitung 7

Im Grunde besteht die vorgestellte Arbeit aus drei einzelnen Studien (an-geordnet in den Kapiteln 3 bis 5), die durch unterschiedliche klinischeFragestellungen hinsichtlich der auditiver Wahrnehmung motiviert sind.In Kapitel 3 wird eine Querschnittsstudie vorgestellt, in der die zentraleHorfahigkeit von Kindern mit einer auditiven Verarbeitungs- und Wahr-nehmungsstorung und von altersgleichen gesunden Kontrollkindern gemes-sen wird. Als elektrophysiologisches Korrelat wird dabei die Mismatch-Ne-gativity (MMN), welche auditive Diskriminationsleistungen abbilden soll,ausgewertet. Neben der Behandlung audiologischer Fragestellungen wer-den Methoden fur die vollautomatische AEP-Analyse vorgestellt und disku-tiert. Diese Methoden basieren auf statistischen Ansatzen und auf Zeit-Frequenz-Analysen.

Sprachverstandnis beruht auf der ausreichenden Geschwindigkeit der Ver-arbeitung des akustischen Eingangssignals. Deswegen ist die objektiveMessbarkeit der individuellen auditiven Verarbeitungsgeschwindigkeit Ge-genstand von Kapitel 4. In dieser Studie wird die zeitliche Auflosungdes zentralen Gehors bei normal-horenden Erwachsenen sowohl psychome-trisch als auch mittels AEP untersucht. Wie sich herausstellt, geben dieAntworten des Gehirns auf ein Kontinuum stummer Lucken in akustischenReizen wertvolle Hinweise auf die Funktionalitat zeitlicher Aspekte desHorens. Von den AEP-Messungen abgeleitete Parameter scheinen die sel-ben Gehirnaktivitaten wie die psychometrischen Tests abzubilden.

Aus neurophysiologischer Sicht ist die Bestimmung der Verarbeitungsstufe,ab welcher sprachspezifische Prozesse erstmals auftreten, von besonderemInteresse. Aus diesem Grunde wird die Verarbeitung bedeutungstragenderSprache in Kapitel 5 untersucht. Dort wird ein AEP-Paradigma vorgestellt,wodurch unter Berucksichtigung der phonetischen Reizeigenschaften dieAntwort des Gehirns auf die Sprachreize enthullt wird. Die raumlicheVerteilung der Gehirnantworten deutet darauf hin, dass sprachspezifischeneuronale Generatoren bereits auf pra-attentiver Ebene aktiv sind. Je-doch scheint vorrangig die phonetische Zusammensetzung des Reizes dieGehirnaktivitat zu pragen. Ermutigt von diesen Ergebnissen wird eineNachbildung sprach-evozierter AEP aus Ereignis-korrelierten Grundbau-steinen vorgeschlagen. Eine Aufklarung der Mechanismen der AEP-Zu-sammensetzung ist von großem neurophysiologischen Belang. Dadurchkonnte eine Entkopplung der auditiven Verarbeitung von sprachspezifi-schen Prozessen ermoglicht werden. Unsere Annahmen werden durch die

8 1 Einleitung

hohe Ubereinstimmung der Nachbildungen mit den original sprach-evoziertenAEP bekraftigt.

2 Fundamentals

2.1 Auditory Perception

The sense of hearing can be divided into a peripheral and a central part.The peripheral part is primarily responsible for the absorption of acousticinputs and their transmission to the actual acoustic organ: the cochlea,where acoustic signals are transformed into electrical neural pulses. Theperipheral hearing resides in the outer, middle and inner ear including thecochlea.

The central part contains functions providing the processing of neural sig-nals in the auditory pathway and auditory cortex. The sum of these pro-cesses is referred to as auditory perception [8]. Thus, auditory perceptiondoes not mean hearing per se, but neural processes of the central ner-vous system, analyzing the acoustic input more and more conscious andintegrating it into complex cognitive processes.

2.1.1 Auditory Pathway

Due to the mechanical design of the cochlea, incoming sound is decomposedinto its spectral components in the inner ear. Hair cells transform soundinto electrical pulses, neighboring frequencies innervate vicinal nerve fibersof the following auditory nerve, indicating the entrance of acoustic stimuliinto the central nervous system. The fibers of the auditory nerve form thespiral ganglion and end in the cochlear nucleus, the first relay station of theauditory pathway [9]. Components of the auditory pathway are depictedin Fig. 2.1.

In the auditory nerve, acoustic information is encoded by an enhanced andsynchronized firing rate of the corresponding hair cell. Thus, sound intensi-ties of different frequencies are ciphered instantaneously [10]. Information

9

10 2 Fundamentals

Cochlear nucleus

Superior olivary complex

Auditory nerve

Nuclei of lateral lemnisci

Cochlea

Inferior colliculus

Medial geniculate body

Auditory cortex

PrimarySecondary

auditory cortex

Vertex

Fro

nta

l

Occip

ital

(a) (b)

Figure 2.1: a) Profile of the human brain depicting fibers and nuclei ofthe auditory pathway, ranging from the cochlea to auditory cortex. b)Lateral view of the human auditory cortex (AC). AC is divided into aprimary field (PAC), which is connected to deeper, thalamic regions, andthe secondary AC which is efferently controlled by association fields.

about the stimulus’ pitch is encoded by exciting the cochlear nucleus at adedicated phase of the stimulus. By the so-called analysis of periodicity,the brain interprets the temporal structure of the spike pattern and com-putes the corresponding pitch. This mechanism works up to a frequency ofapprox. 5 kHz [11]. Starting at cochlear nucleus, neural connections riseto further core areas in the brainstem, either directly (ipsilateral) to theinferior colliculus (IC) in the midbrain, or via the superior olivary complexand the nuclei of lateral lemnisci (contralateral). In the superior olivarycomplex, an inter-aural comparison of the input is conducted. By analyz-ing phase and intensity differences coming from the two ears, sound sourcescan be located. The IC is responsive to specific amplitude modulation fre-quencies and thus is responsible for pitch detection [12]. Furthermore, dueto its neighborhood to the superior colliculus which is part of the visualsystem, auditory and visual information is matched in the IC.

From IC, the auditory pathway continues to the thalamic medial geniculatebody and finally to the primary auditory cortex (PAC) in the Brodmannarea 41 (according to the nomenclature of cortex regions [13]) of cerebrum’s

2.1 Auditory Perception 11

temporal lobe. The PAC resides on the superior temporal gyrus of thetemporal lobe, more precisely, on the Heschl’s gyrus between sylvian fissureand the superior temporal sulcus. The major part of the PAC resides in thesylvian fissure and thus is not visible in Fig. 2.1b. The PAC is surroundedat the outside of the temporal lobe by the secondary AC (SAC). The SACis efferently controlled by modal specific association fields.

Generally, the main part of information processed in the auditory pathwayis transmitted contralaterally. Only few neuron fibers project to ipsilateralnuclei [14].

2.1.2 Auditory Functions

For integral auditory perception, the central hearing has to perform variousfunctions. According to the present understanding of the underlying neuralmechanisms, in audiological practice the following auditory functions aredistinguished [15]:

Sound localization and lateralization

The sound waves of a laterally arriving stimulus reach the contralateralear with a temporal delay. The analysis of the interaural time differenceenables the localization of the sound source in the horizontal plane pre-cisely. The central hearing is able to processes time shifts down to 10–30µs, resulting in an angle of 3 from the mid-line [16].

Auditory discrimination

The ability of a subject to discriminate two different stimuli is assessablefor acoustic and speech sounds, respectively, by psychometric tests. Con-trasts in physical stimulus features, like intensity, frequency, duration, orphonemes, like /da/ vs. /ba/, are applied.

Auditory pattern recognition

The recognition of certain consecutive tone and time intervals (rhythm)and their pitch is inevitable for the processing of prosody in the stream ofspeech.

Intensity

The ability to adapt the sensitivity of loudness to the current situationallows us to perceive stimuli with different intensities in the same quality.

12 2 Fundamentals

Temporal aspects of audition

Most of the mentioned auditory functions contain or depend on unimpairedtemporal processing. Temporal aspects of audition can be subdivided asfollows:

• Temporal resolution

This is the ability to process fast stimulus changes. Changes in speechsignals are often 10 ms or less.

• Temporal masking

Instantly following sounds interfere with each other. The effect ofmasking after a strong sound is called post-masking, and can be ineffect up to 200 ms. The pre-masking, where a sound actually ismasked by a disturbing signal which appears after it, lasts up to 20ms [17].

• Temporal integration

The central hearing has to integrate binaurally arriving acousticevents to one auditory impression. An unilaterally delayed auditoryprocessing results in the disordered merging of the fragments.

• Temporal ordering

The ability to identify the temporal order of two stimuli is mostlydetermined by a detection task if a stimulus was firstly presented tothe left or right ear. The minimum time difference a subject needs fordetecting the correct side is referred to as temporal ordering thresholdand ranges from 15 to 60 ms [16].

• Gap detection

This is the ability to identify brief breaks in the succession of acousticstimuli. Gap detection thresholds of approx. 5 ms are normal [18].

Auditory performance with competing acoustic signals

For the audibility of speech in noise it is crucial to separate importantsounds from unimportant ones. For example, the competing disturbingsignal can result from a noisy environment or from other speakers. Witha conversation of 60–70 dB intensity level, the dialog partner can still beunderstood in a surrounding noise which is 5 dB below the target speechsignal [16].

Auditory performance with degraded acoustic signals

In many natural situations of audition, acoustic signals are not clear, but

2.1 Auditory Perception 13

incomplete or corrupted, e.g. due soft speech or a strong accent. Thus, theauditory system has to provide complex analytic, synthetic, and completingfunctions in order to fill-in unintelligible word fragments.

Attention

Attention is not modal specific, but is vital for unimpaired auditory per-ception. For a detailed consideration about the influence of attention onthe central hearing, see [19].

Auditory memory

Information is processed in several consecutive mental steps. Each stepof processing needs psychic resources [20]. The task of memory is to storeinformation and to make it available if required. With the auditory percep-tion, the auditory sensory memory and the working memory are of specialinterest. The auditory sensory memory possesses huge storage capacity,but only small storage duration. Information is provided until the com-parison with elements of knowledge is completed. The working memory isan active system of cognitive processing. Its capacity can be assessed bythe so-called auditory memory span.

An impairment of auditory functions in presence of an efficient peripheralhearing is diagnosed as central auditory processing disorder (CAPD). Forthe therapy of CAPD three groups of approaches are distinguished [8].

1. Intervention for the improvement of CAPD (exercising approaches).

2. Approaches for the improved compensation of disordered auditoryfunctions (e.g. meta-cognitive approaches).

3. Compensating approaches for the improvement of the acoustic signalquality.

2.1.3 Psychological Model Representation

In the model of Lauer (2001) [21], the distinct auditory functions are as-sociated with distinct processing layers, with respect to the processingdirection. After the acoustic stimulation, sensation, perception, and clas-sification of the stimulus are conducted, resulting in the mental represen-tation of the input. This bottom-up branch of information processing isinfluenced by the factors attention and working memory at all times.

14 2 Fundamentals

Mental Processes

ExpectationsKnowledgeMotivation

ClassificationPattern Recogntion

CompletionSynthesisAnalysis

Perception

SelectionDiscriminationLocalization

Sensation

Sensory Processes

Acoustic stimulation

Working Memory

SequencingStorage

Attention

VigilanceSelective AttentionAlertness

Top-downProcesses

Bottom-upProcesses

Figure 2.2: Psychological model of the auditory perception by Lauer (2001).The auditory perception is represented by parallel, interwoven bottom-upand top-down processes. Starting from the analysis of physical stimulusfeatures up the constitution of a mental representation, auditory pro-cesses change from automatic to cognitive.

Fig. 2.2 shows schematically the interaction of the auditory processes in-volved in the model of Lauer: On the sensational layer, physical stimulusfeatures are transformed in neural activity, while the auditory functionslocalization, discrimination and selection are associated with the percep-tual layer. Not until the layer of classification, the stimulus achieves adistinctive meaning by analysis, synthesis, and completion of the percep-tual representation. Here, the pattern recognition and the alignment of theinput to familiar structures is conducted [21]. Besides these bottom-up pro-cesses, perception is influenced by higher mental top-down functions likemotivation, knowledge, and expectations. Although the auditory processesare depicted serially and hierarchically, Lauer notes that auditory percep-

2.2 Auditory Evoked Potentials 15

tion is a matter of parallel processing with smearing boundaries betweenthe individual layers.

2.2 Auditory Evoked Potentials

2.2.1 Principles

For the first time, in 1924 Hans Berger recorded fluctuations of the elec-trical field on the scalp [22]. He interpreted them as cerebral activity andcalled them electroencephalogram (EEG). Davis (1939) found that the per-ception of sound can alter the EEG of a human listener [23]. This alterationis referred to as auditory evoked potential (AEP). The AEP is a possibilityto visualize the response of the brain to presented stimuli.

1 100 200 500 1000

Latency (ms)

0.5

0.1

5

Am

plit

ude (

µV

)

10 20 502 5

2

1

0.2

0.2

0.5

5

2

1

VERTEXPLUS

III

IIIIV V

VI

NN N

N

0

a b

1

N2

P

P P

P

0

a1

2

Early potentials Middle-latencypotentials

Late potentials

0.1

Figure 2.3: Human auditory evoked potential (AEP) depicted on a log-logscale, after Picton et al. (1974). According to their temporal appearance,AEP components are divided in three ranges with different nomenclature.

As a result of the neural propagation along the auditory pathway, the tem-poral evolution of the AEP can be divided in three time windows, according

16 2 Fundamentals

to the place of cerebral activation. Fig. 2.3 shows the waveform of a typicalhuman AEP in response to a click, plotted on a log-log scale [24]. The de-flections of the waveforms are called AEP components and reflect distinctlevels of auditory perceptions. Early AEP components are labeled withLatin numbers, while late AEP components are labeled according to theirsign (P or N) and either the number of their appearance (as in Fig. 2.3) orthe point of their peaks, e.g. P1-N1-P2, or P50-N100-P200, respectively.AEP components are primarily described by four characteristics:

• The latency (in ms) specifies the point of the peak’s extremal valueand refers to the onset of the presented stimulus. Early AEP com-ponents with a latency of 1–9 ms are generated in the brainstem.Middle-latency (10–50 ms) components are allocated to thalamicstructures of the midbrain. Components of the late AEP (effectivefrom 50 ms) are generated in the cortex (see Fig. 2.3). AEP compo-nents abate around 500 ms after stimulus ending.

• The peak amplitude (in µV ) of AEP components depends on theimpedance of the recording electrodes and of the thickness of thescull. Thus, AEP amplitudes vary inter-individually and for vary-ing measurement equipment. The highest AEP amplitudes can berecorded in young children and add up to 30 µV .

• The relationship of the components among each other is expressedby the waveforms’ morphology. AEP components generated fromdifferent neural processes can overlap in time [25]. The temporalfusion of different AEP components can be revealed when the AEPmorphology is considered (for instance, by the comparison of AEPsto the same stimulus measured at varying attentional states). Hence,the AEP morphology is an important parameter but difficult to quan-tify. One focus of the present work is the development of appropriateapproaches.

• AEPs can be recorded at different places across the scalp. For ex-ample, AEP components can be pronounced prior at the vertex or atlateral sites [25]. Depending on the electrode position, the three AEPcharacteristics mentioned above can alter. Spatial AEP differencesare expressed by the AEP topography. Multi-channel AEP record-ings enable the determination of underlying active neural structuresusing the so-called dipole source analysis [26]. The positioning of the


electrodes is specified in the international 10-20 system [27]. Fig. 2.4shows the electrode sites used in this work.

Left Right

Fp1 Fp2

F7F3 Fz F4

F8

C3T3 Cz C4 T4

P4PzP3P7 P8

O2O1

Tp9 Tp10

F3 C3

Tp9

T3

F7

Fp1

P3 O1

P3

Nasion

Vertex

Inion

FzCz

Pz

Inion

Nasion

Figure 2.4: Spatial distribution of the electrode sites used in this work. Thepositioning of the electrodes is conducted according to the international10-20 system.

The exploration of AEPs generated at the cortex (late AEPs) includingtheir neural generators was advanced through the 1960s and early 1970s.It became apparent, however, that the strong inter-individual variability ofthe cortical AEPs posed limitations for the application in clinical routine.The brainstem AEPs (early AEPs) proved as more stable and robust incontrast to cortical responses [28]. Hence, with the brainstem electricalresponse audiometry (BERA) an objective approach for the determina-tion of the hearing threshold level was developed. With BERA, brainstemresponses to click tones are recorded, which follow a specific waveformmorphology in case of accurate hearing.

In the middle of the 1990s the interest in cortical AEPs (late AEPs) aroseagain, when it became apparent that, as electrophysiological correlates ofthe auditory perception, they can be exploited for the investigation of au-ditory functions in clinical populations. Due to these promising properties,we address the analysis of cortical AEPs exclusively in this work. Thus, inthe following AEP characteristics in a poststimulus interval of 50–500 ms(i.e. time since the onset of the auditory stimulus) are of special interest.

18 2 Fundamentals

2.2.2 Neural Generators

EEG potentials recorded from the scalp are the consequence of post-synapticactivity of brain cells. Synapses are the junctions neurons are linked witheach other for information interchange. Basically, the cortex contains twotypes of brain cells: stellate cells and pyramidal cells [29]. The wide-stretching geometry of the pyramidal cells and their parallel alignmentvertical to the cortex surface are essential for the EEG derivation. Schemat-ically, Fig. 2.5a shows isolated one pyramidal cell. The cell body (soma)is surrounded by the apical and basal dendrites and the axon. The axonrepresents the signal output (efferent). Its synapses are linked with den-drites of following neurons functioning as signal inputs (afferent). Due todifferent ion concentrations in case of idle status, a membrane potentialbetween outside and inside the neuron of 70–80 mV exists. The positiveions are distributed uniformly around the cell membrane, see Fig. 2.5b.The activation of a synapse — elicited by an action potential spreadingalong the axon — results in depolarization (excitatory synapse) or hyper-polarization (inhibitory synapse) of the following cell membrane. The iondistribution around the cell membrane misaligns. As depicted in Fig. 2.5c,in case of depolarization, the sub-synaptic membrane becomes an electriccharge sink, while the post-synaptic membrane becomes an electric chargesource (vice versa at hyper-polarization). Such a polar structure is referredto as electric dipole [30]. Characteristically, the electric dipole implies anelectric field, which can be measured in the space (Fig. 2.5d).

Due to the wide-stretched geometry of the apical dendrites and the enor-mous number of synapses on each neuron, changes in the dipole momentappear very slowly in a range of 10–100 ms. If in the course of subsynapticde- and hyper-polarization, the membrane potential falls below a certainthreshold, an action potential is generated abruptly, diffusing over the axonand innervating succeeding neurons. The electric field elicited from a sin-gle neural dipole alone is too tiny to be registered on the scalp. Not untilmany parallelly oriented pyramidal cells of a neural population are ac-tivated synchronously, the resulting electric field is measurable as EEG.Thus, the EEG fluctuations do not register differentiated information pro-cessing but the level of excitation of neural networks. Consequently, AEPcomponents with negative polarity indicate an enhanced level of excitationof the underlying neural structures, particularly the auditory cortex, while


cell body

basal dendrite

apical dendrite

afferent fibresynapse

initial segmentaxon

a) Pyramidal cell b) Regularly distributed anions

d) Neural dipolec) Active synapse

Equipotentialline

Electricflux line

Scalp

EEG

cell membrane

Figure 2.5: Neural dipoles after synaptic excitation. a) Besides the cellbody, cortical pyramidal cells consist of basal and apical dendrites ab-sorbing incoming synapses, and one axon, sending out electrical pulsesin case of an activated initial segment. b) Synapses are idle, anions aredistributed equidistantly around the neurons in the inter-cellular space.c) Active synapse (depicted bold) on the apical dendrite. Consequently,an anion overbalance appears at the base of the cell. d) As a result,a neural dipole forms. Due to the electric field, a potential differencebetween two electrodes is measurable on the scalp.

20 2 Fundamentals

positive polarity indicates inhibited excitation.

2.2.3 Cortical AEP Recordings

The brain response to an acoustic stimulus is not immediately visible in thecontinuous EEG recording. Since vegetative functions never stop, AEPsare superimposed by so-called background activity during the entire record-ing period of length Ψ. The influence of cortical background activity onthe EEG recording depends highly on the subject’s state of vigilance. Itsamplitude can add up to 200 µV , thus the signal-to-noise ratio (SNR) candecrease to −20dB. Assuming that the cortical response to an acousticstimulus AEP (t) of duration T , as depicted in Fig. 2.3, is available as aresult of signal processing, the SNR can be estimated as

SNR = 10 log

(1T

∫ T

0 (AEP (t))2 dt1Ψ

∫ Ψ

0 (EEG(t))2 dt

)[dB]. (2.1)

Under following assumptions:

• the AEP shows invariant morphology across multiple stimulations,

• the EEG background activity is a stationary noise process,

• the EEG background activity is uncorrelated with the AEP,

the SNR can be increased by two processing steps:a) bandpass filtering of the raw EEG and subsequentlyb) averaging multiple AEPs in response to the same acoustic stimulus.

Since AEP waves are slow (≤ 20 Hz) in contrast to the fluctuations of theraw EEG, bandpass filtering of the EEG signal increases the SNR. High-pass filters of 0.3 Hz cut-off frequency for the suppression of EEG driftsand low-pass filters of 20–100 Hz cut-off frequency, depending on whetherthe fine-grained structures of the AEP components are to be preserved, areused.

Depending on the AEP design, the acoustic stimulus is presented succes-sively 100–1000 times with an inter-stimulus interval (ISI) of approx. 0.5–5 seconds. The recorded EEG signal is segmented in epochs (also called


sweeps) xq which are triggered by the onsets of the stimuli. Despite theintroduction of sophisticated averaging techniques, in most of the cases theresulting AEP waveform is still obtained by creating the arithmetic meanof Q recorded sweeps:

AEP (t) =1

Q

Q∑

q=1

xq(t) (2.2)

In practice, 100–200 sweeps are necessary to reduce the residual noise inthe AEP waveforms to an acceptable degree, while the SNR increases pro-portional with

√Q for non-logarithmic representation of the SNR [31].

The residual SNR can be estimated in different ways. Here, two commonapproaches are mentioned:

1. In the recordings of cortical AEPs, the time frames of the sweepscontain a pre-stimulus time of 100 ms. In this interval, cortical re-sponses to the former stimulus are abated, i.e. the EEG exclusivelyreflects background activity. Hence, the residual SNR can be esti-mated by comparing the power of the AEP with the variance of the(unbiased) pre-stimulus signal.

2. The amount of recorded sweeps are divided into two sub-averages Aand B. The residual SNR is estimated by comparing the commonportion of the sub-averages A + B with the differing portion A − B.

2.2.4 Cortical AEP Components

Two types of cortical AEP components are distinguished: obligatory andcognitive ones. In terms of the above-mentioned psychological model ofthe auditory perception (Fig. 2.2), obligatory components correspond tobottom-up processes, while cognitive components correspond to top-downprocesses.

Incidence, latency and amplitude of the obligatory components are de-termined primarily by acoustic stimulus features. The generators of theobligatory components reside in the primary and secondary auditory cor-tex. Obligatory components can be elicited by clicks, tone bursts, com-plex tones, or speech stimuli. While the peripheral hearing is completely

22 2 Fundamentals

developed with birth, maturation of the central hearing takes until ado-lescence. Thus, the incidence of obligatory components highly depends onage. Fig. 2.6 shows AEP waveforms in response to harmonic tones for dif-ferent age groups, recorded at channel Cz [32]. The waveforms were createdby the so-called grand averaging: determining the mean waveform over allsubjects by arithmetic averaging. Obviously, AEP waveforms become morecomplex with age.

Time (ms)

Am

plit

ude (

µV

)

0 100 200 300 400 500 600

5

0

-5

5

0

-5

5

0

-5

P1

P1

N2

N2

P1 P2

N1 N2

9-y

ear

old

sA

dults

4-y

ear

old

s

Figure 2.6: Grand average AEPs of different age groups (4-year olds, 9-year olds, adults) in response to harmonic tones at channel Cz, fromCeponiene et al. (2002). The different AEP characteristics result fromdifferent states of the maturation of the auditory pathway.

While during childhood, the predominant AEP components are P1 andN2, adult subjects obligatorily exhibit the components P1, N1, P2, andN2. Out of these, the most stable components are N1 and P2 in adults[33], also called N1/P2 complex due to their common appearance. TheN1/P2 complex is thought to represent the synchronous neural activity ofstructures in the thalamic-cortical segment of the central auditory system[34]. The N1, a negative peak occurring approximately 100 ms after stim-


ulus onset, is suggested to reflect sound detection functions [35], since itis sensitive to onset sound features, like intensity and ISI. P2, a positivepeak occurring approximately 200 ms after stimulus onset, is assumed toreflect sound content properties like acoustic or phonetic structures [36].

The characteristics of cognitive AEPs vary with the listener’s attention toand performance on cognitive tasks assigned while responses are recorded.The most prominent cognitive components are the mismatch negativity(MMN) and the P3 [37]. Both are elicited via oddball paradigm and reflectauditory discrimination processes. The oddball paradigm involves a fre-quently presented standard stimulus and a sporadic interspersed deviantstimulus, which can differ from the standard in an arbitrary perceptiblefeature. The ratio between the presentation of standard and deviant stim-ulus raises from 7/1 to 10/1 [38]. MMN is obtained as negative half wave inthe difference signal between the cortical responses to the deviant and thestandard stimuli. It appears around 200 ms in adults [39] and somewhatlater in children [40]. If attention is directed to the presented stimuli, theP3 as positive half wave of around 300 ms latency additionally appears inthe cortical response to the deviant stimulus.

MMN is thought to be associated with automatic, subconscious memoryprocesses [37]. The repetition of the standard stimulus causes a trace inthe auditory sensory memory. Any new incoming stimulus is compared tothe created memory trace. MMN is generated if the deviant stimulus isperceived to differ from the sequence of stimuli before. Hence, the MMNis the neural correlate of an automatic change-detection process that isindependent from attention [41]. A sensory-specific process located in theauditory cortex is suggested to detect the deviant stimulus which thentriggers a frontal process related to a passive attention switch [42].

The magnitude of the MMN correlates with the perceived contrast betweenthe standard and the deviant stimulus [43,44]. The better a subject is ableto distinguish the deviant from the standard stimuli, the larger the MMN[45]. The deviant stimulus may differ from the standard stimulus in atleast one stimulus attribute. In the simplest case, the standard stimulusis a pure tone and the deviant differs in frequency, duration or intensity[40,46–48]. Besides sinusoidal tones, speech stimuli such as vowels [49,50],syllables [39,51,52] or pseudo-words [53] are used.

24 2 Fundamentals

3 Evoked potentials in children with

central auditory processing disorders

3.1 Introduction

The basic elements of language acquisition develop in the early childhood[6]. In case of a specific language impairment (SLI), psychometric testscan holistically assess the state of a subject of this age group, but are notable to diagnose its etiology differentiated. One potential cause of SLI is acentral auditory processing disorder (CAPD), since auditory perception ofspeech requires the central hearing to process complex acoustic structuresprecisely. The grave negative influence of CAPD on the infantile languagedevelopment has extensively been demonstrated. SLI children often showa deficit in the discrimination of auditory stimuli presented in rapid suc-cession [54]. In addition, SLI children have deficits in the identificationof short, noise-succeeded sounds [5]. Also their auditory memory span isreduced significantly [55]. However, the contribution of impaired auditoryfunctions among SLI children is still discussed controversially [7]. Whileobjective tests exist for the peripheral hearing which are irrespective of age(e.g. otoacoustic emissions, BERA), there are no objective tests proceduresready for clinical application for the central hearing — and thus for theauditory perception — to this day. Traditionally, for the German-speakingregion audiologically oriented language tests are applied for the assessmentof the central hearing, like ’Gottinger Kindersprachverstandnistest’ [56],’Mainzer Test’ [57], or ’Oldenburger Kindertest’ [58]. These psychometrictests are subjective, i.e., they depend on the cooperation of the patient.Hence, tremendous need exists for the development of objective hearingtests, like electrophysiological or brain imaging techniques.

In this study, we examined the capability of MMN for clinical applicability.The MMN enables objective measurement of the auditory discrimination

25

26 3 Evoked Potentials in Children with CAPD

ability [59] which is one of the factors assumed to be disturbed in childrenwith CAPD [15]. Up to now, several studies showed that miscellaneousgroups with infantile developmental disturbances exhibit changed MMNsas an indication of delayed central auditory processing. Attenuated MMNamplitudes to pitch deviances were found in children with SLI [46, 47].Also, children with impairments in frequent-word-reading tended to havediminished MMNs [60]. In another study dyslexic children were found tohave smaller MMNs compared to their controls [61]. In a study by Krauset al. (1996) children with bad discrimination abilities of rapid acousticchanges that occur in speech (/dalpha/ vs. /galpha/) also had significantlylower amplitudes and area under the curve (AUC) of the MMN than thehealthy controls [51].

However, examinations with MMN still lead to different and ambivalentresults. For example, no differences between CAPD children and controlsregarding their MMN characteristics in response to speech stimuli werefound by Liasis et al. (2003) [62]. Alonso-Bua et al. (2006) also found nodifference concerning MMN characteristics between children with readingdifficulties and their controls [63]. Chinese school children did not differ inMMN characteristics if they were dyslexics [64]. Uwer et al. (2002) foundattenuated MMN waves in SLI children in response to speech stimuli butno different MMN waveforms in response to tones [52]. The different MMNresults could be due to the fact that the setups of the tests were different,varying contrasts were used or SLI children with no CAPD-issues werechosen for the studies, having the same hearing abilities as the controls.Further, the common way for finding MMNs is analysing the differencewaveform visually for each subject [65]. Usually the existence of the MMNcomponent is detected if a negativity in the difference waveform exceedsthe standard deviation of the prestimulus interval [66, 67].

Aim of this work is the development of an automatic, computer-based ap-proach for the objective identification and classification of CAPD in chil-dren. Conceptually, MMN measurements serve as basis for this work dueto their representation of central hearing processes regardless of cognitiveabilities. A cross-sectional study examining language acquisition and cen-tral hearing in preschoolers provide the fundamental data. Besides MMNmeasurements, an extensive psychometric test battery is conducted con-sisting of approved German language and hearing tests.

3.2 Methods 27

3.2 Methods

3.2.1 Participants

In cooperation with the public health department of Erlangen, preschoolchildren were recruited with suspect language screening results due toschool enrollment. 105 children (age 5;0 to 7;0 years) with suspicion on SLIparticipated in the cross-sectional study. In addition, 13 healthy subjectswere recruited from kindergardens and served as age-matched controls.The study was planned as test battery which was embedded in clinicalroutine and took up to 8 hours, split in two sessions. The test batterywas compiled with respect to holistic sourcing on the one hand and in theface of temporal optimization for stable cooperation of the subjects on theother hand. Fig. 3.1 gives an overview of the compiled test battery. Testsdiagnosing the same medical or psychological aspect are embraced by agray background and a number.

In the beginning, a medical anamnesis was conducted (marked as (1) inFig. 3.1). Parents were asked for indicators they noticed concerning theirchild’s SLI. In block (2), the sense of hearing was examined via pure toneaudiogram [68], tympanogram [69], and otoacoustic emissions (OAE) [70]in order to isolate peripheral hearing disorders. In (3), the psychometrictests ’Heidelberger test of language development (HSET)’ [71], ’Active vo-cabulary test (AWST)’ [72], and ’Bielefeld screening for early diagnosis ofreading and writing disorders (BISC)’ [73], and ’Psycholinguistic analy-sis of infantile speech disorders (PLAKSS)’ [74] served for the assessmentof language acquisition. Children exhibiting a developmental retardation,were captured via the non-verbal scale of ’Culture Fair Intelligence Test 1(CFT-1)’ [75], marked as (4). Such children (IQ<85) were excluded fromthe further study, since appearing language impairments are not referableto auditory disorders. In the remaining children, the auditory perceptionof speech was examined in (5) using ’Gottinger Kindersprachtest in noise’[56] and ’Heidelberger Vorschul-Screening (HVS)’ [76]. Finally, 32 out of105 suspect children were diagnosed with CAPD and participated in theAEP measurements.


Controls Suspect Children

Anamnesis

Pure Tone Audiogram

Tympanogram

DPOAE

HSET

PLAKSS

CFT-1IQ>85 Exclusion

Speech Audiometryin Noise

Auditory evoked Potentials

2

3

4

5

6

1

AWST

BISC

HVS

n

y

Figure 3.1: Test battery for the assessment of CAPD. Language develop-ment and central hearing of SLI children with suspicion on CAPD weretested intensively. Additionally, as tested by CFT-1 subjects exhibitingnon-verbal IQs < 85 were excluded from the study. In contrast, withcontrol children only peripheral and central hearing was examined.

3.2 Methods 29

Besides the CAPD children, psychometrical tests and AEPs were also ap-plied in the 13 controls. Since the controls did not show any risks beforeonly one test per possible syndrome was applied, as depicted in Fig. 3.1.

3.2.2 Stimuli and Procedure

The AEP design for the MMN elicitation used in this study is derivedfrom the multi-deviant design suggested by Naatanen et al. (2004) whichwas applied to adults [66]. Due to the application in preschool children,some modifications from the original paradigm were conducted. One ofthe 5 deviant stimuli (deviation in the direction) was omitted, since forthe participant’s convenience acoustic stimuli were presented binaurallyvia two loudspeakers positioned frontally to the participants (Naatanenet al. (2004) used headphones). Subjects were seated in a comfortablechair in a sound-attenuating and electrically shielded chamber. To ensureinattention to the stimuli during testing, all subjects watched a mutedvideo tape. The recordings were done in three identical sequences. Eachsequence lasted approx. 5 minutes, so the EEG registrations per subjecttook 15 minutes altogether. Between the blocks, breaks of 5 minutes wereprovided. During recordings all children were visually monitored.

The standard stimulus (SS) was a complex tone consisting of 3 sinusoidalcomponents of 500 Hz, 1000 Hz, and 1500 Hz, with an intensity level of67 dB HL (hearing level). Against the fundamental frequency, harmonicswere reduced by 3 dB and 6 dB, respectively. The duration of the stan-dard stimulus was 100 ms including 5 ms rise and fall time. The fourinterspersed deviant stimuli differed from the standard stimulus as follows:

• The ’Frequency Deviant’ (FD) had a 10% increased frequency com-pared to the standard stimulus (⇒ fundamental frequency of 550Hz).

• The sound level of the ’Intensity Deviant’ (ID) was increased by 10dB (⇒ 77 dB).

• The deviant in the signal duration (DD) had a signal length of 25ms (⇒ 75 ms less).

• The ’Gap Deviant’ (GD) was created by inserting a 16 ms gap in themiddle of the standard stimulus.


The discrimination task was simplified by doubling the number (2:1) ofstandard stimuli (Naatanen et al. (2004): 1:1) and by enhancing the con-trast between standard stimulus and the deviant stimuli DD and GD. Thestimuli were presented with an inter-stimulus interval of 500 ms. Withinthe sequence, two standard stimuli were followed by a randomly chosendeviant stimulus. Fig. 3.2 shows exemplarily a 7-second period of the pre-sented stimulus signal. Altogether, three identical sequences of 5 minuteseach were applied. Thus, 1200 standard stimuli as well as 600 deviantstimuli (i.e., each deviant 150 times) were recorded.

SS SS ID SS SS FD SS SS GD SS SS DD SS SS GD . . .

0 1 2 3 4 5 6 7//

Time (s)

. . .

Figure 3.2: AEP paradigm for the elicitation of MMNs in multiple stimulusfeatures. Stimuli are presented with an ISI of 500 ms. After two standardstimuli (SS) a randomly chosen deviant (FD: frequency, DD: duration,ID: intensity, and GD: gap deviant) follows.

3.2.3 EEG Recording

The EEG was derived with Ag/AgCl electrodes which were integrated inan electrode cap with fixed electrode positions (Braincap, Brain ProductsGmbH, Gilching, Germany). The electrode impedances were kept below10 kΩ. Nineteen recording channels were distributed over the entire scalpaccording to the 10-20 system. Additionally, two further electrodes (i.e.,Tp9 and Tp10) were placed at the two mastoids, see Fig. 2.4. Their meanvoltage value served as reference [31]. For eye artefact rejection, the electro-oculogram (EOG) was recorded by an electrode placed under the righteye. EEG and EOG data were collected at a sampling rate of 500 Hzand digitized with 16 bit by an EEG-amplifier (Brain Products GmbH,Gilching, Germany).

For all children the resulting AEPs of each channel were computed as fol-lows: The EEG was 0.13 Hz - 20 Hz bandpass-filtered offline with a slope

3.2 Methods 31

of 12 dB/octave. The entire signal was decomposed in 550 ms sweeps, from50 ms pre-stimulus onset to 500 ms post-stimulus onset. Sweeps includ-ing an EEG or EOG change exceeding ±150 µV were judged as artifactsand omitted from the following averaging. For the visual analysis of thewaveforms, the gained sweeps were averaged separately for each stimulustype. To remove voltage offset, the computed mean voltage of the 50-mspre-stimulus period served as baseline.

3.2.4 SNR Determination

The quality of the AEP measurement was quantified by means of SNR.With the determination of the SNR, the AEP distribution over the scalpwas considered. Hence, the so-called global field power was calculatedwhich is the root-mean-square of the AEP over the 19 derived EEG-channels c as a function of time

RMSAEP (t) =

√√√√ 1

19

19∑

c=1

AEP (c, t)2. (3.1)

As mentioned in Sec. 2.2.3, the SNR was determined by comparing theAEP power within the pre-stimulus interval with the AEP power of thepost-stimulus interval:

SNRAEP = 20 log1

500ms

∫ 500ms

t=0msRMSAEP (t) dt

150ms

∫ 0ms

t=−50msRMSAEP (t) dt

(3.2)

In order not to intersperse the quality of measurement with the amplitudeof an occurring MMN, the individual SNRs were determined for the corticalresponses to the deviant stimuli and not for the difference signals out ofdeviant and standard responses.

3.2.5 Wavelet Transform

This section is an extended version of Martin Burger, Ulrich Hoppe, Pe-ter Kummer, Jorg Lohscheller, Ulrich Eysholdt, and Michael Dollinger,


”Wavelet-based analysis of MMN responses in children”, BiomedizinischeTechnik, Vol. 52, pp. 111–116, 2007.

AEP components are traditionally described by latencies and amplitudesof their peaks. The fine grained structure of AEP waveforms is difficult todescribe and the AEP morphology as a whole is not considered. However,particularly in children the AEP morphology provides important informa-tion about the maturation of the central hearing. Throughout childhoodand adolescence the predominant AEP components diminish, while newcomponents arise [33]. Inter alia, emerging components show themselvesas saddles in the slopes of the AEP waveforms, which only can be detectedwhen the AEP morphology is regarded.

To address this problem in the present study, the measured AEPs in re-sponse to the stimuli SS, FD, DD, ID, and GD were analyzed using discretewavelet transform (DWT). This transform yields the frequency portionsof a signal for various points in time in the form of DWT coefficients.In contrast to the short-time Fourier-transform, DWT exhibits propertiessuitable for AEP waveforms: Regarding low frequencies a good frequencyresolution is achieved (under acceptance of a weak temporal resolution),and with high frequency a good temporal resolution is achieved (underacceptance of a weak resolution in frequency domain).

An efficient calculation of the DWT coefficients is achieved by applying amultiresolution algorithm (MRA) [77]. The MRA is implemented by theso-called subband coder which is schematically depicted in Fig. 3.3. For theanalysis of the different frequency subbands, the signal is passed througha series of high-pass filters (HP) and a series of low-pass filters (LP). Thescale is cut in half from one level to the next by down-sampling by a factorof 2 (↓ 2). Fig. 3.3 shows the applied decomposition algorithm up to level 5.The signal is decomposed into approximation (A1–A5) and correspondingdetail information (D1–D5). Finally, only one approximation (A5) and fivedifferent detail spaces are obtained.

In this work, the signal decomposition and reconstruction is done by Coif-man Wavelets of order N = 2. In Fig. 3.4 LP and HP filters, sampled andshifted versions of the scaling function Φ and the wavelet Ψ, respectively,are depicted. Coifman Wavelets exhibit a range of suitable features for thedecomposition of AEP signals. As required for DWT, they are compactlysupported wavelets [78]. They have a high number of vanishing moments

3.2 Methods 33

LP 2

HP 2

AEP A1 A2 A3 A4A5

D5

D4

D3

D2

D1

LP 2 LP 2 LP 2 LP 2

HP 2

HP 2

HP 2

HP 2

Figure 3.3: Schematic of the subband coder. The input signal is successivelow-pass and high-pass filtered throughout 5 levels resulting in the detailcoefficients D1 - D5 and the approximation coefficients A5.

for Φ (2N) and Ψ (2N −1) for a given support width (6N −1). They haveorthogonal and biorthogonal properties, i.e. there are 2 different waveletsfor decomposition and reconstruction [78]. Coifman Wavelets also exhibitnearly symmetrical behavior and arbitrary regularity [78].

0

0.5

1

0.5

10 2 4 6 8 10

0

0.5

1

0.5

10 2 4 6 8 10

0

0.5

1

0.5

10 2 4 6 8 10

Decomposition

high-pass filterlow-pass filter

Reconstruction

0 2 4 6 8 10

0

0.5

1

0.5

1

high-pass filterlow-pass filter

Figure 3.4: Low-pass and high-pass filters for the decomposition and the re-construction of the AEP using DWT. Low-pass filters are a sampled andshifted version of the scaling function Φ, high-pass filters of the waveletfunction Ψ of coiflet with order N = 2.

AEP Decomposition

To enhance the computational performance, AEP waveforms were reducedto a sampling length of 256 (i.e., power of 2) corresponding to a time inter-val of 512 ms. Therefore, the first 38 ms of the pre-stimulus interval withinthe entire signal length (550 ms) were cut off. The MRA was executed upto level 5 yielding 256 DWT coefficients. The coefficients were subdivided


in the different detail levels D1 to D5 (i.e., number of coefficients: 27, 26,25, 24, 23) and the approximation level A5 (23 coefficients).

AEP Reconstruction

Due to previous 20 Hz low-pass filtering of the AEPs (Sec. 3.2.3), thespaces containing the low frequency attributes were dominant. As a suf-ficient value for AEP reconstruction, 95% of the coefficient power was as-sumed [13], which was reached by exclusively the coefficients of A5 andD5 in each derived AEP. Hence, in the following exclusively the 16 coeffi-cients from A5 and D5 were used for the AEP reconstruction. Exemplarily,Fig. 3.5 shows the reconstructed AEP waveform with those 16 coefficients.Fig. 3.5a exhibits an AEP with good SNR and its reconstruction. Fig. 3.5bshows the reconstruction of a noisy AEP reproducing the low frequencycomponents. Regarding each channel of all participants, the reconstructedsignals exhibited a correlation of (87 ± 3)% with the original AEP wave-forms.

P1

N2

AEPREC P1

N2

0

(a) (b)

Time (ms)Time (ms)

U (

µV

)

U (

µV

)

Figure 3.5: DWT reconstruction (solid waveform) using the coefficients ofA5 and D5 of a) a smooth and b) a noisy AEP waveform (dashed).

Extraction of AEP characteristics

For the extraction of the latencies and amplitudes of occurring AEP com-ponents within the cortical responses to the stimuli SS, FD, DD, ID, andGD, AEP waveforms were generated by means of the wavelet reconstruc-tion procedure mentioned above (using the coefficients of A5 and D5). Ina first step, AEP peaks were detected by visual inspection of the grand

3.2 Methods 35

mean of these AEP waveforms, i.e. by averaging the waveforms of thesame stimulus over the individual subjects of the group under investiga-tion. Grand mean waveforms were created for the control and the CAPDgroup separately. Then, the grand mean waveforms served as template forthe detection of the AEP peaks of the individual subjects. The individualAEP peaks were detected automatically as maxima/minima (depending onwhether positive or negative half wave) in a time interval of ±20ms aroundthe tracked grand mean peaks.

For the description of the AEP morphology a set of certain wavelet coef-ficients was selected and summarized into a feature vector. The selectionof the coefficients depended on the investigated waveform portion and itsstructure. The generated feature vectors of the individual participants wereclassified via cluster analysis. Finally, the members of the resulting classeswere examined concerning their division into control or subject.

3.2.6 H1-Method

Traditionally, the MMN component is determined by visual inspection ofthe AEP waveforms and their difference signal [31]. Correct MMN detec-tion highly depends on the SNR of the constituting AEPs and thus on thenumber of presented sweeps. This approach requires experienced examinersand thus is inefficient for a possible clinical application. Furthermore, theaveraging procedure does not consider the variance over the single sweeps.Thus, we developed an automatic sweep-based approach for MMN detec-tion which we called H1-method. Summarized, the H1-method determinestime intervals, in which the standard response statistically differs from theresponses of the various deviant stimuli.

In a first step the sampling values of the single recorded sweeps si[n] inresponse to each stimulus were pooled for each discrete point in time n =1, . . . , 250 independently (500 ms post-stimulus interval x 500 s−1 EEGsampling rate), resulting in 250 samples of standard and deviant responseseach. Fig. 3.6 shows the composition of the samples of I sweeps in responseto the standard stimulus sstani [n] and J sweeps in response to one of the

deviants sdevj [n] with the points n = k and n = k + l highlighted.


Pooling

s

Deviant ResponseStandard Response

k

No

Time [n]

[µV

]

k+l

H ?0

[µV

][µ

V]

Time [n]

stan1

sstan2

sstanI

sdev2

me

an

/SD

Ye

s

sdev1

sdevJ

k k+l

k k+l

H1

1

0

Time [n]

Channel Fz

k k+l

1

0

Time [n]

logicalAND

k k+l

H1

1

0

Time [n]

Channel Cz

Figure 3.6: Schematic illustration of the H1-method: Pooling of amplitudesat the points k and k + l in every deviant and standard stimulus elicitedsweep. Application of statistical t-tests (H0: The mean value of the AEPsof the standard sweeps and deviant sweeps are significantly different fromeach other at point k but not at point k + l).

3.2 Methods 37

Then, a comparison between the samples of standard and deviant responsewas conducted by Student’s two-tailed t-test with a level of significance ofα = 0.05. The output of the t-test is a 250-point signal consisting of eitherzero or one for each point n, depending on whether the null hypothesis H0

(i.e., means are equal) can be rejected or not:

H1[n] =

1

0

,

,

Reject H0 at point n

otherwise. (3.3)

For example, in Fig. 3.6 H1[k] = 0 since the samples out of sstani [k] and

sdevj [k] do not differ significantly, and H1[k + l] = 1 since the sample

sstani [k + l] clearly differs from the sample sdevj [k + l].

The signal H1[n] can be determined for each EEG channel independently.In case of weak conducting electrodes, channel-specific noise due to highelectrode impedances distorts the measurement. Thus, the definite signalH1[n] was created by a point-by-point logical AND-operation of the signalsH1[n] of various channels including MMN. Out of the 19 used channels,Fz and Cz were selected due to their outstanding reflection of corticalresponses. Applying a large number of t-tests causes statistical α-errors[79], i.e. an increase of false positive decision for H1[n]. Due to physiologicalreasons two constraints were defined for the current approach to avoid falsepositive MMN detections:

• The duration of the MMN half-wave varies strongly in this age groupdue to different AEP designs but can be assumed to last for approx.100 ms [80]. H1[k] = 1 only was accepted, if at least 10 consecu-tive sampling points (i.e. 20 ms, also suggested as minimal MMN

duration by [39]) in the neighborhood of k exhibited H1[n] = 1.

• The analysis of the signal H1[n] was limited to the time interval from140 ms to 400 ms post stimulus. The temporal limits were chosenbecause with children of this age– MMN does not peak before 140 ms [81] and– the so-called Late Discriminative Negativity (LDN) emerges after400 ms and covers the MMN [63].


The LDN is an unstable AEP component evoked in an oddball paradigm(see Sec. 2.2.4) as follower of the MMN [38] in children. Since LDN doesnot seem to be linked to sensory sound discrimination [50], it is not subjectof the present study.

Estimation of MMN Characteristics from H1

Based on the H1-method several MMN characteristics were estimated,some of them derived once per participant and deviant and others de-rived additionally for each EEG channel. MMN characteristics determinedonce per participant and deviant, were:

1. Presence of MMN is detected, if H1[n] is unequal to zero for at least20 ms

2. Onset latency of MMN is the first occurring point of H1[n] of (1)unequal to zero.

3. Duration of MMN is the time period with H1[n] = 1. If H1[n] = 1shows more than one significant interval (of ≥ 20ms), they are addedup.

MMN characteristics estimated for each channel based on the AEP differ-ence waveforms (deviant response − standard response) of the consideredchannel:

4. The MMN peak amplitude is the minimal value (MMN is a neg-ative half wave) of the difference signal out of deviant and standardresponse within all intervals of evident MMN.

5. The MMN peak latency is the point of the peak amplitude.

6. MMN Area-under-the-curve is the integral of the difference signalin all intervals of evident MMN.

Validation of the H1-Method

The individual AEP waveforms containing the MMN were corporately in-spected by three clinical experts. The inspection was carried out at channelFz for each deviant type in all 45 participants. The resultant MMN char-acteristics were compared with the results generated by the H1-method, inorder to validate this new approach.

3.3 Results 39

Statistical Comparisons

Preliminary analyses of variance (ANOVAs) compared the extracted channel-specific MMN characteristics (4.–6.) across the different electrode sites inorder to determine the scalp distribution of MMN evoked in controls andsubjects. Thus, a preselection of channels adequate for further analysis canbe conducted. The presence of the MMN for all deviant stimuli – vary-ing in each participant from zero (none of the deviants detected) to four(all deviants detected) – was compared between the two clinical groups(percental distribution and t-test). Since the preliminary ANOVA yieldedthat the most prominent MMNs are observable at fronto-central sites inboth groups, the following analysis was restricted to the most representa-tive channel Fz. 2-way ANOVA comparing the MMN characteristics withrespect to the factors group (2 levels: controls, subjects) and deviant type(4 levels: FD, DD, ID, GD) was conducted.

Lateralization effects were examined by comparing the channel-specificMMN characteristics (4.–6.) of contra-lateral electrode sites. Three pairsof laterally corresponding channels (F3↔F4, C3↔C4, T3↔T4) were an-alyzed using paired Student’s two-tailed t-tests. The electrode sites aredepicted in Fig. 2.4.

As the applied parametrical tests require normal distribution, Kolmogorov-Smirnov tests were applied to verify normal distribution of each MMNcharacteristic (other than MMN presence). A probability for equal meansof p ≤ 0.05 was accepted as a statistically significant difference. If ANOVAshowed significant effects, Student-Newman-Keuls’ (SNK) post-hoc testswere performed to extract subgroups of equal mean.

3.3 Results

3.3.1 Cortical Responses to the presented Stimuli

Grand mean AEP waveforms of channel Fz in response to the standardstimulus and the four deviant stimuli are shown in Fig. 3.7, separatelyfor the subjects (solid) and the controls (dashed). As one can see, AEPwaveforms of all stimuli are predominated by the components P1 and N2.


Globally, P1 latency is at approx. 120 ms, its amplitude reaches 8 µV onthe average. The N2 can be found from 240 ms to 300 ms varying acrossthe stimuli with amplitudes of -10 µV . In the cortical responses to SS andID a saddle-structure is observable in the falling edge of P1. Strikingly,AEPs of the standard stimulus as well as of the deviant stimuli are rathersimilar between both groups.

U (

µV

)

Time (ms)

ControlsSubjects

10

-10

0

15

5

-5

-150 200 400

SS FD

U (

µV

)

Time (ms)

ID

DD

Time (ms)

GD

P1

N2

P1

N2

P1

N2

P1

N2

P1

N2

10

-10

0

15

5

-5

-150 200 400

10

-10

0

15

5

-5

-150 200 400

10

-10

0

15

5

-5

-150 200 400

10

-10

0

15

5

-5

-150 200 400

Figure 3.7: Grand mean AEPs of the subjects (solid waveforms) and thecontrols (dashed) at channel Fz. AEPs evoked by the standard stimulus(SS) and the deviants in frequency (FD), duration (DD), intensity (ID),and gap (GD) are shown.

The detailed latencies and amplitudes of the AEP components are listed inTab. 3.1. As indicated in Fig. 3.7, also t-tests revealed no significant differ-ence in any of the peak latencies and amplitudes, with one exception: Onaverage, controls exhibited higher N2 amplitudes in DD than the subjects(marked with (*) in Tab. 3.1).

The saddle which is apparent in the falling edge of P1, is not pronouncedin all participants equally. In order to consider noise as potential cause ofthe P1 saddles, AEPs’ SNR was estimated by the standard deviation ofthe pre-stimulus intervals, averaging out 0.69±0.24 µV at channel Fz. A

3.3 Results 41

Table 3.1: Mean ± standard deviation of the P1/N2 latencies (in ms) andamplitudes (in µV ) of the control group and the subjects at channel Fz inresponse to standard stimulus (SS), and frequency (FD), duration (DD),intensity (ID), and gap deviant (GD).

P1SS FD DD ID GD

Controls 117±19 116±21 120±11 124±26 118±12Lat (ms)

Subjects 121±16 118±16 128±13 119±21 121±17Controls 8.3±2.5 8.7±3.3 8.9±3.5 8.8±2.7 7.7±3.6

Ampl (µV )Subjects 8.3±3.4 9.3±3.8 9.0±4.0 8.5±3.8 9.0±3.7

N2SS FD DD ID GD

Controls 280±9 277±17 241±32 305±26 305±25Lat (ms)

Subjects 287±16 284±20 245±24 306±21 308±24Controls -4.5±3.5 -10.8±3.7 -5.0±3.6* -10.5±5.9 -8.6±4.8

Ampl (µV )Subjects -3.6±2.8 -10.0±4.9 -2.5±3.4* -10.3±5.8 -7.7±5.5

t-test between corresp. samples of subjects and controls: *p < 0.05, non signif. else

saddle characteristic (notch – second peak) of 1.57±1.07 µV was computedfor the registered double peaks. This means an SNR > 6 dB which shouldexclude noise as basis for this observation and indicate a physiologicalrelevant maturational process.

The morphology of the P1 can be described by the wavelet coefficientsof D5 covering the relevant time interval. Written as feature vector, it isf = (D5(2), D5(3), D5(4), D5(5)). As revealed by two-step cluster analysis(using Schwarz Bayesian criterion) [82], AEPs can be separated into twoclasses by means of their feature vectors f . Exemplarily for the corticalresponse to SS at channel Fz, Fig. 3.8a shows the elements of f determinedin each individual. The analysis of the cortical responses to the deviantsis critical, since mismatch effects may distort the obligatory P1 response.According to the cluster analysis, 16 participants exhibiting large values forthe elements of f (class I) and 29 participants with tiny elements of f (classII) are pooled. The cross-classified table Tab. 3.2 shows the distribution ofthe classes I and II among the subjects and controls.


Table 3.2: Crosstab of the classes I/II expressing the P1 morphology amongsubjects and controls.

Controls SubjectsClass I 7 22 Σ 29Class II 6 10 Σ 16

Σ 13 Σ 32

Apparently from Tab. 3.2 and verified by means of discriminant analy-sis [83], an assignment of individual saddle characteristics to the divisioncontrol/subject was not observable.

U (

µV

)

Time (ms)

10

0

5

-5

0 200 400Time (ms)

10

0

5

-5

0 200 400-10 -10

A coeff.5

1 2 3 4

0

10

20

-10

-20

(a)

(b)

Coeff. lo

ad

Class I Class II

A coeff.5

1 2 3 4

0

10

20

-10

-20

Coeff. lo

ad

Figure 3.8: Class I and II as determined by cluster analysis. a) Scatter plotof the underlying features – DWT coefficients D5(2), D5(3), D5(4), D5(5)– of each individual. b) Grand mean responses to SS at channel Fz ofthe two classes.

3.3 Results 43

3.3.2 MMN

Fig. 3.7 shows that on group level, N2 seems to be more negative in thedeviant waveforms (FD, ID, and GD) than in the standard waveform (SS).This is an indicator for MMN which is contained in the response to thedeviants and superimposes the obligatory N2. In Fig. 3.9, the differencesignals out of grand mean deviant AEPs and the standard AEP of channelFz are depicted for subjects (solid waveforms) and controls (dashed wave-forms) in order to isolate the MMN from the obligatory AEP components.In each deviant type, the negativity holds from approx. 200 ms to 500 ms.However, the waveform of DD − SS exhibits two sections of negativity incontrast to the other deviant types with one continuous and cumulativenegativity.

U (

µV

)

Time (ms) Time (ms)

ControlsSubjects

GD - SSDD - SSFD - SS ID - SS

Time (ms)

10

-10

0

15

5

-5

-150 200 400

Time (ms)

(a) (b) (c) (d)

10

-10

0

15

5

-5

-150 200 400

10

-10

0

15

5

-5

-150 200 400

10

-10

0

15

5

-5

-150 200 400

Figure 3.9: Difference signals of the grand mean waveforms (FD − SS),(DD − SS), (ID − SS), and (GD − SS), exhibited by the subjects (solid)and controls (dashed).

MMN Detection via H1-Method and via visual Inspection

A higher incidence of MMN was found by visual inspection of the individualwaveforms at channel Fz compared to the sweep-based H1-method: while98 MMNs were concordantly detected visually and statistically, another 13MMNs were visually identified that could not be detected statistically. In69 cases neither the sweep-based method nor visual inspection revealed aMMN. Hence, a consistency of 93% of both methods was achieved, withthe statistical method to be more conservative.


The quality of the individual AEP measurements was assessed by the SNRof the cortical response to the distinct deviant types. The individual SNRsranged from 3.7 dB to 25.8 dB. As shown in Fig. 3.10, the intervals ofSNR of detected MMN (8.9 dB – 25.8 dB, depicted as circles) and non-identified MMN (3.7 dB – 19.3 dB, depicted as ∗), overlap for the mostparts. Apparently, a lower SNR bound exists (≤8.9 dB) for the detectioncapability of the H1-method. Within the detected MMNs, the AUC ofthe MMN, normalized to the AUC of the cortical response to the standardstimulus ranged from 0.7 to 7.8. Fig. 3.10 does not indicate any dependencyof the relative AUC of MMN from the SNR.

0 5 10 15 20 25 30

SNR [dB]

detected MMNno MMN identified

0

1

2

3

4

5

6

7

8

AUC

AUCstan

MMN

Figure 3.10: Area under the curve (AUC) of the elicited MMN (normalizedto the AUC of standard waveform in the same time interval) as functionof the SNR of the deviant stimulus AEPs. If MMN was not detected therespective SNR is depicted as ∗.

Significant negativities of each individual participant and each deviant asdetermined by the H1-method are depicted in Fig. 3.11. Time coursesof H1[n] = 1 are plotted as black bars. Significant negativities outsidethe temporal MMN window (marked as gray areas) are classified as onsetpercept (< 140 ms) or as LDN (> 400 ms) and thus are not considered forMMN data analysis.

3.3 Results 45

Frequency (FD) Duration (DD) Intensity (ID) Gap (GD)

1

10

0 100 200 300 400 500

Contr

ols

Time (ms)

1

Time (ms)Time (ms)Time (ms)

Subje

cts

10

20

30

0 100 200 300 400 500 0 100 200 300 400 500 0 100 200 300 400 500

0 100 200 300 400 5000 100 200 300 400 5000 100 200 300 400 5000 100 200 300 400 500

Figure 3.11: Results of the H1-method yielding signals containing black bars.The black bars represent regions of significant difference between standardand deviant response. The MMN-relevant time interval of 140 – 400 msis highlighted. The results are split into the control group and the CAPDgroup.

Within the MMN window only a fraction of the individuals exhibit MMNsto the different deviant types, holding for both clinical groups. In the 13controls, 10 FD, 8 DD, 9 ID, and 10 GD MMNs were present, while in the32 subjects 21 FD, 13 DD, 21 ID, and 17 GD MMNs appeared. Hence,the percental fraction of MMNs in the control group exceeded that of theCAPD group (77%, 62%, 69%, 77% in controls vs. 66%, 41%, 66%, 53%in subjects).

Regarding the quantity of MMNs evoked in each individual, controls slightlybut not significantly exceeded the subjects (mean of 2.85 vs. 2.25 out of4 possible MMNs per individual). In detail, the distribution of the MMNquantity (0, 1, 2, 3, 4 identified MMNs) within the control group was 0, 1,3, 6, 3 and in the CAPD group 5, 3, 8, 11, 5.


Comparison of the MMN characteristics between both groups

In order to illustrate the scalp distribution of the MMN, Fig. 3.12 showsthe difference waveforms (deviant response − standard response) of everyelectrode site averaged over the deviant types FD, ID, and GD. DD differ-ence waveform is excluded from averaging due to its distinct morphology,see Fig 3.9. Controls (dashed) and CAPD children (solid) exhibit simi-lar waveforms in each channel. ANOVA testing the scalp distribution ofthe MMN peak amplitude revealed that both groups showed symmetricMMNs with fronto-central focus, with Fz being the predominant chan-nel. Tab. 3.3 lists mean and standard deviation of the extracted MMNcharacteristic (items 2–6 in the enumeration of Sec. 3.2.6) for both groupsseparately. In Tab. 3.3, peak latency, peak amplitude, and AUC whichdepend on the regarded channel, were derived from Fz. In the analysis ofthe MMN characteristics only measurements with evident MMN were in-volved. The 2-way ANOVA revealed that none of the MMN characteristicsshowed significant group-dependent differences.

Table 3.3: Mean ± standard deviation of MMN duration, onset and peak la-tency, peak amplitude and AUC (the latter three derived from Fz) evokedby FD, DD, ID, and GD.

FD DD ID GDDuration Controls 129±81 71±52 124±77 126±76(ms) Subjects 124±61 70±45 95±38 99±63Onset lat. Controls 226±74 179±15** 254±75 262±73(ms) Subjects 220±35 216±64** 283±34 245±63Peak lat. Controls 310±60 225±53 332±39 366±30(ms) Subjects 290±50 255±82 344±40 332±59Peak ampl. Controls -10.8±3.9 -8.7±2.7 -10.9±4.8 -10.6±3.7(µV ) Subjects -10.5±3.3 -8.1±1.9 -10.2±3.1 -8.6±3.1AUC Controls 1.29±1.09 0.56±0.53* 1.18±0.93 1.18±0.96(10−6V s) Subjects 1.10±0.59 0.47±0.34* 0.86±0.53 0.77±0.60Post-hoc analysis:Significant (p<0.05*, p<0.01**) differences between the deviant types.

3.3 Results 47

Time (ms)

-10µV

10µV

500

Fp1 Fp2

F3

Fz

F4

F7 F8

Cz

Pz

C3 C4

O1 O2

P3 P4

T3 T4

T7 T8

SubjectsControls

Figure 3.12: Scalp distribution of the difference waveform: deviant response− standard response averaged over the deviant types FD, ID, and GDfor both groups, controls (dashed waveforms) and CAPD children (solidwaveforms).


Comparison of the MMN characteristics between the deviant types

Differences occurred with respect to the deviant type. The MMN onsetand peak latency vary significantly (p < 0.01) between the deviant types.ANOVA post-hoc analysis showed that MMNs evoked by the durationoccur earlier than those of the residual deviant types. MMNs elicited byID and GD peak at ≈ 340 ms and thus later than FD (≈ 300 ms) and DD(≈ 240 ms). Also revealed via post-hoc analysis, DD exhibit smaller AUCin contrast to the other deviants (p < 0.05), which is referred to shorterMMN duration rather than smaller amplitudes.

3.4 Discussion

In the presented study, MMN characteristics were obtained in response tochanges in tone stimuli in subjects diagnosed with CAPD and controls.The objective was to examine if MMN characteristics reflect central audi-tory processing disorders and consequently if the MMN serves as potentialclinical tool for the assessment of central hearing functions.

Properties of the cortical responses to the various stimuli (especially themorphology of the AEP waveforms) were determined using wavelet de-composition. A multi-deviant MMN paradigm similar to the one thathas been used for time-effective MMN derivation in adults before [66] wassuccessfully applied to preschool children. Four deviants in frequency, du-ration, intensity, and the presence of a gap were presented in one sequence(Fig. 3.2). In contrast to other studies, which choose a wide age rangeand neglected aspects of maturation [84], exclusively preschool children ina very limited age range from 5;0 to 7;0 years were examined.

The AEPs of the examined children showed the typical waveforms for theirage [32]. Differences in AEP characteristics — like peak latencies andamplitudes — between the two clinical groups (controls or CAPD) werenot observable. However, the morphology of the P1 component indicatedthe stage of maturation of the individual auditory systems. In the case of adouble-peak P1 the emergence of a N1 is observed which is the predominantAEP component in adults.

3.4 Discussion 49

H1-Method vs. Visual Inspection

Identification of the MMN as well as the extraction of the MMN char-acteristics was accomplished using an automatic statistical method (H1-method). The H1-method was validated by contrasting its MMN detectionperformance with MMNs identified by visual inspection of the waveforms.In 93 % of all waveforms the two methods arrived at the same conclusions.Thus, the H1-method is applicable for the analysis of AEPs elicited fromchildren in the present age group. There were 13 MMNs found by visualinspection that could not be supported by the H1-method. Hence, meetingthe criteria of the H1-method for an individual MMN is more difficult com-pared to the common way of visual inspection of difference waveforms. Forthe visual identification of MMN, the examiner has to exclude EEG back-ground noise as possible reason for a negative deflection in the investigatedtime interval. While visual inspection highly depends on the quality of theAEP measurement (expressed by SNR), the H1-method is inured to SNRas far as possible. However, a lower SNR bound exists for the detectioncapability of the H1-method, as shown in Fig. 3.10.

In contrast to visual inspection of the AEPs, the H1-method is an objectiveapproach. Involving two channels, it is less vulnerable to channel-specificnoise compared to visual inspection. As a statistical method, it is basedon single sweeps and does not only depend on the mean value but also onvariance and sample size. Thus, the number of presented sweeps and con-sequently the time of measurement could be reduced in an online approachin case of recordings with good SNR. However, within the investigatedpopulation, systematically increased variances could not be observed instatistically non-significant intervals.

To identify an MMN by the statistical method, the values of deviant andstandard stimulus evoked sweeps must be significantly different from eachother (p=0.05) for at least 10 consecutive points in the two channels Fzand Cz. A non-significant result for the presence of MMN therefore doesnot necessarily indicate the absence of MMN. In further studies an adap-tation of the H1 parameters (probability, interval, involved channels) toindividuals will be examined.


Characteristics of the MMN

According to the H1-method, the quantity of evoked MMNs varied acrossthe participants. Earlier studies, in which stimuli were presented in anoddball paradigm, also showed that there were healthy children in whomnot all MMNs were evident [40, 48]. The fact that not all possible MMNswere evident may be due to different perceptual capabilities of the subjects.

Latencies of the derived AEP components in response to the various stimuliare in line with the results of other studies examining this age-group [32,85].Amplitudes of different studies are more difficult to compare: Varyinginter-stimulus intervals and stimulus frequencies produce amplitudes thatcan range from ± 1 µV [33] up to ± 10 µV [32].

In most of the cases in the present study, the lower bound (140 ms postonset) of the chosen MMN interval was not crossed by the H1-signal, seeFig. 3.11. This is as expected, since Ceponiene et al. (2004) noticed theMMN to start in preschool children around 150 ms [81]. A clear temporalseparation of MMN and LDN was not observable consistently in the regionof 400 ms, as observed by Alonso-Bua et al. (2006) [63].

The peak latency of the response to the duration deviant (DD) was signif-icantly shorter than those of the other deviants. An explanation for thisphenomenon could be that the contrast of DD was not appropriate. It ispossible that the putative MMN was not a result of the cortical mismatchprocess but of the formation of DD compared to the standard stimulus.Therefore, the difference waveform supposedly was caused by diverse oblig-atory AEP components independent of whether a stimulus mismatch wasneurally detected or not.

Comparing the incidence of MMN in the two groups of children, the con-trols showed more MMNs. This is consistent with Sharma et al.’s (2006)findings that controls had more MMN than children with reading disor-ders, which often are the result of impaired auditory perception [59]. Thisindicates that the subjects are in fact inferior discriminators.

3.5 Conclusions 51

3.5 Conclusions

The introduced H1-method provides an objective alternative to the sub-jective and error-prone visual inspection of averaged signals from clinicalexperts. According to our MMN results, CAPD children reveal demon-strable substandard discrimination performance [47]. However, on thelevel of individual patients the selectivity of the MMN for CAPD diag-nostics is insufficient, since auditory discrimination alone does not coverCAPD etiology [8]. The AEP morphology which was examined by means ofwavelet coefficients reflects the individual maturation of the auditory path-way. However, the status of maturation does not indicate the individualperformance in auditory perception.

4 Gap Detection

4.1 Introduction

The human hearing is optimized for the processing of speech. In liter-ary language segmentation information is provided by letters, words, andsentences. In contrast, segmentation of speech is managed by auditorytemporal processing which can be defined as the perception of the tempo-ral characteristics of a sound or the alteration of durational characteristicswithin a restricted or defined time interval [18].

In a hierarchical model of the temporal processing, Poppel (1978) classifiedfour layers [86]:

1. Temporal Resolution refers to the ability of the auditory systemto respond to rapid changes in the envelope of a sound stimulus overtime [87]. The individual temporal resolution can be expressed bythe fusion threshold which is the minimal inter-stimulus interval atwhich two consecutive stimuli can be perceived as separate events.

2. Temporal Ordering is determined by the ordering threshold (OT).That OT corresponds to the minimal interval at which the order oftwo stimuli can be identified. In contrast to the fusion threshold, OTis not modal-specific and is suggested to represent a central neuralclock of around 20 to 40ms in healthy adults [88].

3. Subjective Now is a time interval of approx. 2 to 3 seconds whichis subjectively experienced as presence. In this interval, acousticinformation is integrated and perceived as coherent gestalt.

4. Experience of Duration is the ability to assess time intervalslonger than 3 seconds and depends on memory.

In clinical practice, the individual fusion threshold is determined by meansof the detection of shortest-duration silent gaps within continuous stimuli

53

54 4 Gap Detection

(gap detection threshold, GT) and ranges between 2 and 10 ms [89]. Theinvestigation of temporal resolution by gap detection has firstly been intro-duced by Garner in 1947 [90]. Gap detection abilities correlate with speechperception acuity [35]. Thus, it may be that similar or overlapping neuralprocesses are employed both in detecting brief silent gaps and in resolvingthe fine grained structure of the speech signal.

Particularly in pedaudiology, the experience with gap detection is limited.Trehub et al. (1995) found a maturation effect in children for gap detec-tion tasks [91]. In addition, there is evidence that children with learningdisabilities may demonstrate abnormal temporal resolution abilities basedon gap detection procedures [92].

Most recently, the relation between behavioral gap detection tasks andauditory evoked potentials – like MMN [93,94] or BERA [95,96] – have beeninvestigated, without finding strong correlations. Gage & Roberts (2000)found that the M100 – the magnetoencephalographic (MEG) counterpartof the N1 – is sensitive to silent gaps at the sound onset within a briefand finite window of integration (< 40 ms) [97]. They found that theneural processes underlying the formation of the M100 were sensitive tostimulus features such as peak intensity and integrated energy within thetemporal window of integration. In contrast, the amplitude of the M100was dominated by the onset of the stimulus.

Both OT, and GT are easy to extract in cooperative adult subjects via psy-chometric test procedures, but considerably more difficult in children. Aimof this study was to assess if electrophysiological correlatives to OT andGT can be found in order to replace the subjective psychometric test pro-cedures against the objective measurement of auditory evoked potentials(AEPs). Therefore, AEPs were derived from normal hearing adults usingan AEP design which considers both, temporal ordering and temporal res-olution. Also, GTs were determined in all subjects via a psychometric gapdetection procedure.

4.2 Method 55

4.2 Method

4.2.1 Participants

Nine right-handed healthy adults (six male, 25 – 33 years) participated inthe study. Informed consent was obtained from all subjects. A pure toneaudiogram was derived from each subject, in order to ascertain peripheralhearing.

4.2.2 Subjective Test: Gap Detection Procedure

For the assessment of the subjects’ temporal resolution, a cooperation-dependent gap detection procedure was implemented according to Musieket al., 2005 [18]. The subjects had to listen to white noise stimuli, whichwere interspersed by short, silent gaps of different lengths. The task of thesubjects was to press a button in case of a perceived silent gap. Altogether,38 six-second noise segments were presented alternating with silent inter-rupts of 5 seconds in order to relax hearing and concentration. Hence, thetest took less than 7 minutes.

In each segment 0 to 3 silent gaps were embedded with gap length τ rangingfrom 2 to 20 ms. The location, number, and duration of the gaps pernoise segment varied randomly throughout the test. The first gap withina segment was not presented until the first second passed; the shortestinterval between two consecutive gaps always exceeded 1000 ms. 60 gapscould potentially be detected dividing up in each 6 gaps of 10 differentdurations τ : 2, 3, 4, 5, 6, 8, 10, 12, 15, and 20 ms. The response time forthe subjects was limited to 500 ms.

The computer-generated noise used in the test consisted of uniformly dis-tributed values between -1 and +1 with a sampling rate of 16 kHz. Thenoise was turned on and off instantaneously, i.e., no ramps were includedat the edges of the interspersed silent gaps. The noise segments were pre-sented binaurally via headphones Beyer DT48 with an intensity of 70 dBHL in a soundproof chamber.

The individual gap detection threshold GT was determined by fitting thelogistic model function:

56 4 Gap Detection

f(τ) =1

1 + em(GT−τ)(4.1)

to the percentage of correctly detected gaps yi of length τi. GT representsthe 50% threshold of correct hits with m as the gradient of f(τ) in GT .

Eq. (4.1) was fitted by minimizing the mean squared error:

minm,GT

∥∥∥∥∥∥∥∥∥

y1

y2...

y10

−

11+em(GT−τ1)

11+em(GT−τ2)

...1

1+em(GT−τ10)

∥∥∥∥∥∥∥∥∥

2

2

(4.2)

by means of Gauss-Newton iteration [98].

4.2.3 Objective Test: AEP Measurement

AEP Stimuli and Design

Stimuli were 1 kHz sinusoidal tones of 250 ms duration, including 5 ms riseand fall time (cosine profile), presented binaurally via headphones BeyerDT48 with an intensity of 70 dB HL. Gaps of varying durations 0, 2, 5,7, 10, 15, and 20 ms were inserted into the stimuli in two experimentalconditions:

1. at a point 10 ms post-stimulus onset (the 10-ms masker condition),and

2. at a point 40 ms post-stimulus onset (the 40-ms masker condition).

Analogously to the notation τ in the subjective GT estimation, the differentgap durations of this design are called τ obj in the sequel.

The presented stimuli are shown in Fig. 4.1. At the edges of the gaps,stimuli were modulated with 2-ms ramps (cosine profile) to avoid aliasingeffects. The resulting thirteen stimuli were presented 185 times each ina random order with an interstimulus interval of 1000 ms in a passivelistening paradigm.

4.2 Method 57

10 20 30 40 50 60 250

Time (ms)

10-ms Masker Condition

Ga

p D

ura

tio

n (

ms)

15

20

10

8

5

2

0 10 20 30 40 50 60 250

Time (ms)

40-ms Masker Condition

15

20

10

8

5

2

0

(a) (b)

0 0

Figure 4.1: Stimuli used in the AEP design. Gaps either begin at 10 ms(a) or 40 ms (b), gap lengths varied from 0 to 20 ms.

EEG Recording and Feature Extraction

The EEG was derived with Ag/AgCl electrodes which were integrated in anelectrode cap (Easy Cap, FMS, Herrsching-Breitbrunn, Germany) with 10fixed electrode positions: F3, Fz, F4, FCz, C3, Cz, C4, Pz, TP9, TP10. Foreye artifact rejection, the electro-oculogram (EOG) was recorded by elec-trodes placed above and besides the right eye. The electrode impedanceswere kept below 5 kΩ. EEG and EOG data were collected at a samplingrate of 500 Hz and digitalized with 16 bit by an EEG amplifier (BrainAmp,Brain Products, Gilching, Germany). The recording window included 100ms prestimulus and 700 ms poststimulus time.

The EEG was 0.3 to 50 Hz bandpass-filtered offline with a slope of 12dB/octave. During data acquisition, all channels were referenced to FCz.Offline, data were re-referenced to the mean of TP9 and TP10. Thus, fordata analysis the seven channels F3, Fz, F4, C3, Cz, C4, and Pz wereregarded. Sweeps with artifacts exceeding levels of ±75µV were rejected.The remaining sweeps were averaged separately for each stimulus and pres-timulus baselined. For each stimulus, grand mean waveforms were com-posed and grand mean latencies of the AEP components P1, N1, and P2were identified. The individual peak latencies were automatically deter-mined in a time interval of the central latencies ±20 ms. Peak amplitudes

58 4 Gap Detection

were calculated as a mean voltage of a 20 ms period centered at the indi-vidual peak latencies.

Data Analysis

Dependencies of P1, N1, and P2 latencies as well as the N1-P2 interpeakamplitude on the gap duration τ obj were examined by Pearson’s product-moment correlation test. The influence of the masker condition on the AEPcharacteristics and possible interactions with τ obj was analyzed via 2-wayANOVA with masker condition (2 levels: 10-ms masker, 40-ms masker)and τ obj (7 levels: gap durations of 0, 2, 5, 8, 10, 15, and 20 ms) as factors.

Possible dependencies of the AEPs on the individual gap detection abilitieswere also analyzed by Pearson’s product-moment correlation test. There-fore, parameters were introduced which integratively consider the courseof AEP characteristics along the varying stimulus features. The gradi-ents of the regression lines of the investigated AEP characteristics overthe underlying stimulus gap length τ obj served as those parameters. AEPcharacteristics, for which the gradient of the regression line was analyzed,were latencies and amplitudes of the components P1, N1, and P2 for bothmasker conditions separately. Since channel Cz turned out to elicit themost prominent AEP waveforms, the mentioned statistical approaches ex-clusively included samples of this electrode site.

For the investigation of hemispheric asymmetries, paired t-test were con-ducted comparing the N1-P2 interpeak amplitudes of opposite electrodesites F3 ↔ F4, and C3 ↔ C4. The paired t-tests were conducted for 10-msand 40-ms masker condition separately.

4.3 Results 59

4.3 Results

4.3.1 Individual Gap Detection Threshold

The results of the gap detection task of the 9 adult normal-hearings arelisted in Tab. 4.1. The individual gap detection thresholds GT s reside inan interval of 4.3 to 6.0 ms. Hence, the group is strongly homogeneousregarding temporal resolution abilities. GT s of 4 to 6 ms with normalhearings are in accordance with Musiek et al. (2005) who introduced theapplied test procedure.

Table 4.1: Gap detection test protocol for each subject. Maximally 6 hitscan be achieved per gap duration. The individual gap detection thresholdis listed in the column ”GT”.

Gap Duration (ms)Subject

2 3 4 5 6 8 10 12 15 20Total Score GT

1 0 0 2 4 5 6 6 6 6 6 41 4.62 0 0 0 0 4 6 6 6 6 6 34 6.03 0 0 1 4 6 6 6 6 6 6 41 4.74 0 0 1 1 4 5 6 6 6 6 35 5.85 0 0 1 4 5 6 6 6 6 6 40 4.76 0 0 0 3 4 5 6 6 6 6 36 5.47 0 0 2 5 6 6 6 6 6 6 43 4.38 0 0 0 3 4 6 6 6 6 6 37 5.39 0 0 2 2 6 6 6 6 6 6 40 5.0

Fig. 4.2 shows exemplarily the grand average hit rate (in %, denoted as ∗)over the applied gap lengths τ . Across the entire group, Eq. (4.2) yieldsGT = 5.1 ms, m = 1.58 ms−1 for the model function.

60 4 Gap Detection

0

25

50

75

100

Gap length (ms)ô

Hit r

ate

(in

%)

2 153 4 5 6 8 10 12 20

*******

***

*

*

**

50% threshold

Gap detectionthreshold GT

1

1 + e1.58 (5.1- )ô

Figure 4.2: Average gap hit rate (∗) over the applied gap lengths τ . GT(5.1 ms) is the intercept point of the 50% threshold with the logistic modelfunction 1/(1 + em(GT−τ)).

4.3.2 AEP Characteristics

The most prominent AEPs were measured at channel Cz. Thus, the follow-ing results correspond to this electrode site exclusively. Grand mean AEPwaveforms in response to the different gap stimuli are depicted in Fig. 4.3,where the detected peaks are labeled with a circle. For all stimuli, grandmean AEPs are predominated by the obligatory components P1, N1, andP2. Despite similar overall morphologies, AEPs vary in their characteristiccomponents as function of the used stimuli.

4.3 Results 61

0 100 200 300 400 500 600 700

Time (ms) Time (ms)

0 100 200 300 400 500 600 700

2µV

10-ms Masker 40-ms Masker

Gap D

ura

tion

ô

no Gap

2-ms Gap

5-ms Gap

8-ms Gap

10-ms Gap

15-ms Gap

20-ms Gap

no Gap

2-ms Gap

5-ms Gap

8-ms Gap

10-ms Gap

15-ms Gap

20-ms Gap2µV

(a) (b)

Gap

113 ms

93 ms 93 ms

82 ms

Figure 4.3: Grand mean AEP waveforms in response to the stimuli with

varying τ obj at channel Cz in the a) 10-ms masker condition and b) 40-ms masker condition. In (a), the dashed lines point out the increase ofthe N1 and P2 latencies as function of τ obj.

Fig. 4.4 shows box-whisker plots of the AEPs’ latencies and interpeak am-plitudes for both maskers as function of the gap length τ obj. As gatheredfrom Fig. 4.4a) and c), a strong dependency of the N1 and P2 latencyon τ obj is apparent in 10-ms masker condition but not in 40-ms maskercondition.

As determined by Pearson’s product-moment test, the correlation betweenτ obj and N1, P2 latencies is highly significant on the level of individualsubjects for the 10-ms masker. The correlation coefficient is r = 0.54***for P2, and r = 0.49*** for N1. Despite inter-individual deviations of thelatencies between the subjects, pronounced within-subject correlations arepresent in each subject. In the 40-ms masker, only the latency of the N1component offers a weak but significant correlation with τ obj (r = 0.29*).

62 4 Gap Detection

100

200

120

220

140

80

160

60

180

Late

ncy (

ms)

40

Gap Duration (ms)0 2 5 8 10 15 20

P2

N1

P1

ì

í

î

ì

í

î

ìíî

10-ms Masker

100

120

140

80

160

60

180

Late

ncy (

ms)

40

Gap Duration (ms)

0 2 5 8 10 15 20

P2

N1

P1

ìíî

40-ms Masker

ìíî

ì

í

î

(a) (c)

200

220

2

Inte

rpeak

Am

plit

ude (

µV

)

Gap Duration (ms)

0 2 5 8 10 15 20

(d)

1

4

3

6

5

8

7

2

Inte

rpeak

Am

plit

ude (

µV

)

Gap Duration (ms)

0 2 5 8 10 15 20

(b)

1

4

3

6

5

8

7

Figure 4.4: Box-whisker plot of P1, N1, and P2 latencies and N1/P2 in-terpeak amplitudes over the applied gap lengths for 10-ms masker (a, b)and 40-ms masker (c, d).

In Tab. 4.2, N1/P2 latencies and amplitudes are listed itemized accord-ing to the single stimulus features. Comparing the AEP amplitudes withrespect to the evoking stimuli, with 5.7 ± 1.2µV the 0-ms gap stimuluselicited the strongest N1-P2 interpeak amplitudes, exceeding the AEPs tothe residual stimuli significantly (F = 6.0, p < 0.001). In both masker con-ditions, diminishing N1-P2 interpeak amplitudes as function of increasinggap lengths are observable, ranging from 4.6± 1.7µV at 2-ms gap down to3.3 ± 1.5µV at 20-ms gap. The correlation coefficient between gap lengthand N1-P2 interpeak amplitude is r = −0.34** for 10-ms maskers and

4.3 Results 63

r = −0.52*** for 40-ms maskers. N1-P2 interpeak amplitudes are signif-icantly smaller in the 40-ms maskers (F = 5.6, p = 0.02). Particularly,the P2 amplitude diminishes strongly as function of the gap length in the40-ms maskers, see Fig. 4.3.

Table 4.2: Mean ± standard deviation of the N1 and P2 latency and am-plitude as function of the various gap lengths and masker conditions atchannel Cz.

Gap Duration (ms)Masker

0 2 5 8 10 15 20

Latency (in ms)

N110-ms 110±10 111±10 111±12 114±10 116±9 123±9

40-ms103±8

102±8 103±8 102±9 105±10 105±10 104±10

P210-ms 160±19 165±19 167±22 173±17 184±14 185±14

40-ms160±10

156±10 157±17 161±18 156±15 156±15 149±10

Amplitude (in µV )

N110-ms -2.3±1.1 -2.1±1.2 -1.5±1.5 -1.4±1.2 -2.4±1.6 -2.4±1.7

40-ms-2.7±1.5

-2.0±1.6 -1.9±1.8 -2.4±1.5 -2.2±1.0 -2.6±1.3 -2.9±1.9

P210-ms 2.5±1.2 2.6±1.5 2.8±1.4 2.8±1.2 1.7±1.5 1.8±1.3

40-ms3.0±1.5

2.4±1.2 2.3±1.3 1.7±1.9 1.3±1.3 1.1±1.4 -0.4±1.8

Fig. 4.5 shows means and standard deviation of the N1-P2 interpeak ampli-tudes of the single electrode sites. The enhanced amplitudes in the 10-msmaskers (dark columns) compared to the 40-ms maskers (bright columns)are apparent in each channel. Considering AEP topology, amplitudes in thechannels F3, Fz, F4, and C3, C4 are only slightly diminished against Cz. Incontrast, AEPs in Pz are distinctively smaller than in the other channels.Despite the strong inter-individual variance, in both masker conditions aleft-hemispheric overbalance of the N1-P2 interpeak amplitudes is presentat central sites (C3>C4), but not at frontal sites (F3 ≈ F4). This lateralasymmetry is enhanced in the 40-ms masker (C3: 3.3 ± 1.4µV vs. C4:2.8 ± 1.3µV , p < 0.001) compared to the 10-ms masker (C3: 4.0 ± 1.1µVvs. C4: 3.7 ± 1.4µV , p = 0.03).

64 4 Gap Detection

N1

-P2

In

terp

ea

kA

mp

litu

de

(µ

V)

F3 Fz F4

C3 Cz C4

F3 Fz F4

Pz

40-ms Masker

10-ms Masker

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

Figure 4.5: Mean ± standard deviation of the N1-P2 interpeak amplitudesat the single electrode sites for 10-ms maskers (dark columns) and 40-msmaskers (bright columns). At lateral sites (F3, F4, C3, C4) interpeakamplitudes of the opposite channels are denoted in the background for ahemispheric comparison.

4.3.3 AEP Correlation with the Gap Detection Threshold

Individual cortical responses and corresponding GT s were compared forpotential correlations. As AEP characteristics, N1/P2 latencies and am-plitudes in response to all stimuli as well as the gradients of their regres-sion lines as function of the rising gap lengths were analyzed. None of theanalyzed AEP characteristics showed a significant correlation with the in-dividual GT . But the best correlation (r = 0.71, p = 0.051) exhibited thegradient of the P2 latencies in the 10-ms masker condition ranging from0.38 to 1.14 among the individuals. In Fig. 4.6, the scatter of P2 latenciesof each subject S1, . . . , S9 (kept apart using different symbols like ∗, ⋄, or) over the underlying gap lengths is plotted. Additionally, the regressionline is depicted as arrow ր over individual GT . In subject S1, an outlier(cortical response to the 20-ms gap) was excluded. Apparently, in ”better

4.4 Discussion 65

perceivers”, i.e. in subjects with lower GT , the increase of the P2 latenciesas function of the gap length is weaker.

Gap Detection Threshold (ms)

P2 L

ate

ncy (

ms)

*

*

***

**

125

115

120

130

140

150

135

155

145

4.3 4.6 4.7 5.3 5.4 5.8 6.0

0 5 10 202 8 15

Gap Length (ms)

Regression line of P2 latency

P2 latencies to the variousgap lengths of one individual

* , ,

Outlier

5.0

, ,, ,

S1

S2

S3

S4

S5

S6

S7

S8

S9

Figure 4.6: Individual P2 latencies (e.g. ∗, ⋄, or ) in response to the seriesof gap stimuli over the individual gap detection threshold. ր depicts thegradient of the regression of each individual P2 series ∆lP2.

4.4 Discussion

Goal of the presented study was to investigate, whether psychometric pro-cedures testing auditory functions can be substituted by objective elec-trophysiological measurements. This is of interest for patients who arenot able to cooperate but depend on differentiated diagnoses, like chil-dren with impaired language acquisition. For this goal, temporal aspects

66 4 Gap Detection

of audition were investigated both psychometric and per AEP in normal-hearing adults. The applied AEP design, which was introduced by Gageet al. (2006) for MEG [99], considers both length and position of the gapsthe stimuli are supplied with.

Typically for adults, the acquired AEP waveforms were dominated by theobligatory components P1, N1, and P2 in all subjects in the present study.Despite the consistent AEP morphology across subjects and stimuli, char-acteristics of AEP components varied systematically with the presentedgap features.

Most remarkably, N1 latency prolongation – and as a consequence P2 la-tency prolongation – correlates significantly with the length of the gapwhen inserted after 10 ms, but not when inserted after 40 ms. N1 reflectssound onset features, like intensity or duration, within a finite time inter-val [25]. Gage et al. (2006) suggested that this finite ’temporal window ofintegration’ spans approx. over the initial 35 ms of the stimulus [99]. Sincein the present study, 40-ms maskers did not affect the N1 latency, our find-ings support the view that stimulus features occurring after the temporalwindow of integration are not reflected in the N1 anymore. Nevertheless,also neural processing of stimulus modulations after 40 ms is representedin the AEP, especially, in the P2 amplitudes which diminish as function ofthe gap length.

The temporal ordering threshold (OT) ranging between 20 and 40 ms,is an indicator for a central neural clock [88]. Regarding speech, withinthis period, the fine-grained structures of phonemes which are the basicmodules of speech are processed. Stimulus modulations beyond OT maybe assigned to a new temporal block by the central neural clock and thushandled as new acoustic event. In this case, further obligatory componentsare evoked in addition to the P1, N1, and P2 responses to the initial soundonset. Overlapping AEPs within one stimulus response have already beenobserved in earlier studies and are termed acoustic change complex (ACC)[100]. The ACC is supposed to be composed of different N1/P2 componentsreflecting the acoustic changes across the entire stimulus [34]. The alteredP2 amplitude as function of gap length found in the present study indicatesthe presence of an ACC in response to stimulus modulations beyond 40 ms.

Analyzing the N1/P2 interpeak amplitude of the single EEG channels, aleft-hemispheric overbalance was detected, most distinctive in the 40-ms

4.5 Conclusions 67

masker condition. Since this overbalance was only observed at centralelectrode sites (C3 > C4) but not at frontal electrode sites (F3 ≈ F4), carehas to be taken in the interpretation of this effect.

As expected for normal-hearing individuals, the gap detection thresholdsGT did not vary remarkably in the participating subjects (4.3 < GT <6.0). However, dependencies between individual temporal auditory resolu-tion and underlying neural correlates as reflected by AEPs could be dis-covered. Although with 9 subjects a small sample was examined, a trendwas observable that in ”good perceivers”, i.e. short GT s, the P2 latenciesare relatively close together compared to ”bad perceivers”. Shortened P2latencies can be interpreted as reduced neural effort in the detection of theperceived gap duration. This trend has to be verified in further studieswith greater sample sizes.

4.5 Conclusions

Temporal aspects of audition are reflected in obligatory AEP components.Hence, objective AEP designs are principally able to substitute psycho-metric procedures testing the temporal resolution of the central hearing.Although a number of electrophysiological correlatives of auditory percep-tion are well-known today, a test design using cortical AEPs has not beenestablished yet for clinical routine, due to the strong inter-individual vari-ability of AEP waveforms. Advantageously, the presented approach doesnot depend on inter-individual comparisons of AEPs confining the clinicalapplicability. Provided that patients with impaired central auditory pro-cessing reveal modified cortical responses, the presented approach couldserve as diagnostic tool for the assessment of temporal resolution.

68 4 Gap Detection

5 Decomposition of Speech-Evoked

Potentials

Up to now the current work has concentrated on cortical potentials evokedby simple, synthetically generated stimuli. Synthetic sounds have beenused in order to selectively investigate electrophysiological correlatives ofthe single auditory functions apart from each other. Inter alia, this ap-proach should support differentiated diagnostics in case of language im-pairment. However, in the auditory perception of speech the major part ofauditory functions (described in detail in Sec. 2.1.2) are involved simultane-ously. The interaction of auditory functions required for speech perceptioncan be investigated more globally by means of the cortical response to com-plex acoustic structures of natural speech sounds rather than to syntheticstimuli. Hence, in this chapter the perception of speech is investigateddirectly by the measurement of speech-evoked potentials.

A condensed and shortened version of this chapter is submitted to the Jour-nal of the Acoustical Society of America: ”Effects of consonant-vowel tran-sitions in speech stimuli on the N1 and P2 in adults”, from Martin Burger,Jorg Lohscheller, Ulrich Hoppe, Ulrich Eysholdt, and Michael Dollinger.Other parts of this chapter, submitted to the Journal of the AmericanAuditory Society (Ear & Hearing): ”The influence of temporal stimuluschanges on speech-evoked potentials revealed by approximations of tone-evoked waveforms”, from Martin Burger, Ulrich Hoppe, Jorg Lohscheller,Ulrich Eysholdt, and Michael Dollinger, are under review.

5.1 Introduction

The processing of rapid temporal and spectral changes contained in speechis of outstanding interest, since disorders in temporal processing are sup-

69

70 5 Decomposition of Speech-Evoked Potentials

posed to be often responsible for impaired speech perception. Affected in-dividuals offer a deficit in the discrimination of auditory stimuli presentedin rapid succession [54]. Several investigators have examined the possi-bility of using AEPs to determine the individual discrimination ability ofphonetic structures in speech sounds [101–103]. Due to the complex acous-tic structures of natural speech, speech-evoked potentials exhibit a morecomplex morphology as AEPs elicited by synthetic sounds. Particularly,the influence of spectral and temporal speech sound features, i.e. formantgradients of consonant/vocal-transitions and voice onset-time (VOT), onAEP have been examined [101, 104–106]. VOT is defined as the intervalbetween the release from stop closure and the onset of laryngeal pulsing[107].

In most of the studies, speech-specific AEP modulations were observed inthe components N1 and MMN. Sanders et al. (2002) demonstrated thatthe N1 indexes segmentation during the perception of continuous speech[108]. Sharma and colleagues investigated the morphology of speech-evokedpotentials depending on the behavioral perception of stop consonants. An-alyzing the cortical responses to a /da/-/ta/ continuum they found thatstimuli with short VOTs (0–30 ms) evoked a single N1, whereas stimuliwith long VOTs (50–80 ms) evoked two distinct negative components (N1’and N1). This discontinuity in AEP morphology was in line with the behav-ioral perception of voiceless sounds and thus it was suggested to representan electrophysiological correlate of categorical perception [101]. However,in another study using a /ga/-/ka/ continuum as stimuli, the change inN1 morphology to a double-peaked component did not signal behavioralperception of a voiceless sound. This result indicated that the minimumVOT value of 40 ms required for the temporal separation of N1 depends onacoustic properties of the stimulus rather than the perceptual categoriza-tion [102]. Martin et al. (1999) investigated cortical potentials in responseto syllables with different formant transitions embedded in masking noise[109]. Audible stimuli evoked an N1, which systematically changed withstimulus energy, i.e. sound onset features, but not with behavioral discrim-ination. Nevertheless, the N1 has also been associated with the processingof the phonetic structure of speech sounds [25]. Also cognitive effects, likepriming or training, modulate the N1 specific to speech [105,110].

MMN and MMNm (the MEG counterpart of the MMN) studies indicatedthat already at auditory sensory level the perception of speech exceeds non-

5.1 Introduction 71

phonetic perception [111, 112]. The MMN amplitude corresponded to thebehavioral discrimination of syllables /ba/ and /da/ embedded in maskingnoise [109]. Also, MMNm amplitudes were diminished in Korean listen-ers perceiving /da/ and /ta/ as allophone compared to Russian listeners,perceiving /da/ and /ta/ as distinct phonemes with respect to VOT [113].

Besides modulations in AEP components, also the elicitation of temporallyoverlapping AEP patterns in response to speech sounds has been observed:Ostroff and colleagues (1998) compared the cortical response to the syllable/sei/ with the cortical responses to the sibilant /s/ and to the vowel /ei/[114]. They ascertained that the response to /sei/ was a combination ofthe AEPs to the onsets of the two constituent phonemes /s/ and /ei/.These overlapping AEPs within one stimulus response have been termedacoustic change complex (ACC) [100]. As mentioned in Sec. 4.4, the ACCis supposed to be composed of different N1/P2 complexes reflecting theacoustic changes across the entire stimulus [34].

Overall, the N1 and MMN modulations in the studies mentioned aboveindicate that speech-evoked potentials reflect both the analysis of acous-tic/phonetic stimulus features and speech-specific neural processes. How-ever, the extent of speech processing highly depends on the attention di-rected to the speech input [115–117]. In case of directed attention to speechcontents, additional AEP components appear which have do be consideredindependently from the obligatory components (for an overview, see [118]).Thus, for determining perceptual abilities of speech, first of all, it has tobe verified, if speech-specific generators are present already on auditorysensory level, i.e. without involvement of higher cognitive functions.

Aim of the present study was to examine to which extent speech-specificprocessing occurs already on auditory sensory level in contrast to acous-tic processing. For this purpose, AEPs to five monosyllabic words werecompared with AEPs to noise stimuli with the same temporal envelope ina pre-attentive measurement setup with adults. For a systematic analysisof the cortical responses, the speech stimuli were chosen with respect tovarying VOTs. In a second step, the influence of the acoustic stimulusstructures on the auditory processing (reflected in the AEPs) was investi-gated. Encouraged by the findings of the ACC, our attempt was to emulatespeech-evoked potentials by superimposing two N1/P2 complexes and tocompare the so created waveforms with the recorded potentials.


5.2 Methods

5.2.1 Participants

The participants were 7 normal-hearing, right-handed monolingual nativeGerman speakers (3 female, 4 male). The age range was 22 to 27 years.None was on medication at that time. All participants signed an informedconsent.

5.2.2 Stimuli and Procedure

In this study, three types of stimuli were used to elicit electrophysiologicalresponses:

• A tone stimulus consisting of a 1 kHz sine-burst of 300 ms durationincluding 5 ms rise and fall time.

• Speech stimuli consisting of five monosyllabic words naturally pro-duced by a male speaker, taken from Freiburger speech discriminationtest [119]. The stimuli were Ei /a:i/, Bett /bet/, Dieb /di:b/, Pult/pult/, and Tau /tau/ meaning egg, bed, thief, desk, and dew. Thedurations of the stimuli Ei, Bett, Dieb, Pult, and Tau were 544ms, 430 ms, 501 ms, 567 ms, and 580 ms respectively. The speechstimuli exhibited a bandwidth of 8 kHz and were digitized with 14bit at a sampling rate of 20000 s−1. Except Ei, all speech stimulistarted with an initial stop consonant, and were chosen with respectto their varying VOT. The VOTs were determined as VOT(Ei) =0 ms, VOT(Bett) = 35 ms, VOT(Dieb) = 60 ms, VOT(Pult) =80 ms, and VOT(Tau) = 105 ms.

• The speech stimuli served as raw material for the synthesis of thenoise stimuli. Noise stimuli were created by randomly multiplyingthe discrete samples of the speech stimuli with ±1, and subsequent 8kHz low-pass filtering. Fig. 5.1 shows exemplarily the generation ofthe noise stimulus corresponding to Dieb. Thus, any phonetic infor-mation was removed from the speech stimuli, whereas the intensitywas held constant. By this means, the synthesized noise stimuli EiN ,BettN , DiebN , PultN , and TauN were generated.

5.2 Methods 73

100 200 300 400 5000 100 200 300 400 5000

Speech stimulus:Dieb

Corresponding noisestimulus: DiebN

Time (ms) Time (ms)

Figure 5.1: Speech stimulus Dieb and synthesized noise stimulus DiebN .Both stimuli exhibit the same temporal envelope, but phonetic informa-tion is discarded in the noise stimulus.

Spectrograms of the presented speech and noise stimuli (further on referredto as speech–noise pairs) are depicted in Fig. 5.2. The speech stimuli mainlyconsisted of frequencies lower than 2 kHz, whereas the noise stimuli weredistributed over the entire bandwidth of 8 kHz. For equal presentationloudness, the applied stimuli were calibrated with respect to their rootmean squares to the 1 kHz tone stimulus with an intensity level of 70dB SPL (see ANSI S3.6, 1996 [120]) and presented via headphones (BeyerDT48).

The tone stimulus was presented 120 times in one block. Speech–noise pairswere presented in four blocks of 400 sounds each. Within the block, speech–noise pairs were presented 40 times successively. Thus, each stimulus waspresented 160 times. The inter-stimulus interval varied randomly between1400 and 2100 ms for both, tone bursts and speech/noise stimuli.

Subjects were tested in a soundproof chamber. In order to minimize effectsof the subjects’ state of attention on the applied stimuli, they were askedto watch a soundless movie presented on a TV screen and breaks wereincluded every eight minutes. Altogether, the EEG registrations took 60minutes per subject.

74 5 Decomposition of Speech-Evoked PotentialsF

requency (

kH

z)

Time (ms)

Ei

Ei Bett

Bett Dieb

Dieb Pult

Pult

N N NN Tau

Tau

N

(a) (b) (c) (d) (e)

Fre

quency (

kH

z)

2

4

6

8

00 200 400

Time (ms) Time (ms) Time (ms) Time (ms)

2

4

6

8

00 200 400

2

4

6

8

00 200 400

2

4

6

8

00 200 400

2

4

6

8

00 200 400

2

4

6

8

00 200 400

2

4

6

8

00 200 400

2

4

6

8

00 200 400

2

4

6

8

00 200 400

2

4

6

8

00 200 400

Figure 5.2: Spectral view of the presented stimuli. Top: Naturally spokenmonosyllabic words. Bottom: Synthetic noise sounds, derived from themonosyllabic words. Bandwidth of the stimuli is 8 kHz.

5.2.3 EEG Recording

The EEG was derived with Ag/AgCl electrodes which were integrated inan electrode cap (Easy Cap, FMS, Herrsching-Breitbrunn, Germany) with30 fixed electrode positions (Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2,F7, F8, T3, T4, P7, P8, Fz, FCz, Cz, Pz, FC1, FC2, CP1, CP2, FC5, FC6,CP5, CP6, TP9, TP10). For eye artifact rejection, EOG was recorded byan electrode placed under the right eye. The electrode impedances werekept below 5 kΩ. EEG and EOG data were collected at a sampling rateof 500 Hz and digitalized with 16 bit by an EEG-amplifier (BrainAmp,Brain Products, Gilching, Germany). The recording window included 100ms prestimulus and 600 ms poststimulus time. The EEG was 0.13–20Hz band-pass filtered offline with a slope of 12 dB/octave. During dataacquisition, all channels were referenced to FCz. Offline, data were re-referenced to the mean of TP9 and TP10. Sweeps with artifacts measuringhigher than ±75 µV were rejected. The remaining sweeps were averagedseparately for each stimulus and prestimulus baselined.

5.2 Methods 75

5.2.4 AEP Peak Detection

Grand mean waveforms served as template for the detection of the AEPpeaks of the individual subjects. Peaks of the grand mean AEPs weretracked by visual inspection in the interval of 100–300 ms post onset (ap-prox. time interval of the N1/P2 complex extended by VOT of the speechstimuli). The number of pronounced peaks N varied among the differentspeech and noise stimuli. The AEP peaks of the individual subjects weredetected automatically as maxima/minima (depending on whether posi-tive or negative half wave) in a time interval of ±40 ms around the trackedgrand mean peaks. The peak detection was initially conducted at channelCz, where response amplitudes were largest. Peaks of the residual channelswere tracked in a time interval of ±24 ms around center latencies derivedfrom Cz.

5.2.5 Comparison of Speech- and Noise-Evoked

Potentials

In a first step, statistical comparative tests of the AEP characteristicswere carried out for speech and noise stimuli separately. In a second step,cortical responses to speech and corresponding noise sounds were analyzedand compared among each other.

Since the number of occurring components N can vary across the stimuli, itis not possible to compare peak latencies and amplitudes directly. Instead,the latency of only N1 and the interpeak amplitude, i.e. the AEP totalamplitude, of the distinct speech and noise elicited AEPs were examinedby one-way ANOVA. Student-Newman-Keuls’ (SNK) post hoc tests werecarried out for the determination of equal-mean subsets. For the exami-nation of hemispheric differences, interpeak amplitudes of the lateral scalpsites (T3, FC5, C3 vs. T4, FC6, C4) were compared via paired t–tests.

For each subject time intervals were determined, in which speech and cor-responding noise responses statistically differed. The approach is similar tothat described in section 3.2.6 (H1-method). As sole differences, two-tailedinstead of one-tailed t–tests were conducted and the MMN-typical phys-iological constraints were not required. By this means, the signal H1[n]adopts three values:


H1[n] =

−1 : AEP speech[n] < AEP noise[n]0 : Accept H0 at point n

+1 : AEP speech[n] > AEP noise[n](5.1)

where H0 is the null hypothesis ”means are equal”.

Difference signals were created by subtracting the noise elicited AEPs fromthe corresponding speech-elicited AEPs. On group level, latencies and am-plitudes of occurring components in the difference signals were determinedvisually. On individual level, peaks within the difference signals were auto-matically determined in a time interval of ±30 ms around the peaks of thegrand average signal. Amplitudes were tested for significant appearance viaone-tailed t–test against a mean of zero and examined for correlation withthe VOTs of the underlying speech stimuli via Spearman rank correlationanalysis.

5.2.6 Synthetic Waveform Optimization

Aim of this design step was to identify those acoustic changes which resultin event-related neural activity. Encouraged by the findings concerningACC in the contemporary literature [34, 100, 114], our attempt was toemulate speech-evoked potentials by superimposing two N1/P2 complexesand to compare the so created waveforms with the recorded potentials.

Synthetic waveforms AEP synth were constructed for each of the appliedspeech stimuli, Ei, Dieb, Bett, Pult, and Tau, and compared to therespective measured speech-evoked potentials AEP speech. As basic mod-ules for the construction of AEP synth, the cortical responses to the tonebursts AEP burst were used. Construction of AEP synth and comparisonwith AEP speech were performed on both grand mean and individual level.

As shown in Fig. 5.3, AEP synth was composed of two modules AEP burst,one started at time t0 = 0, in order to reflect the response of the stimulusonset, the other was delayed with the VOT of the regarded speech soundin order to reflect the onset of the vowel. As equation, this superpositionis written as follows:

AEP synth(t) = k1 · AEP burst(t) + k2 · AEP burst(t − V OT ). (5.2)

5.2 Methods 77

k AEP (t-VOT)2

k AEP (t)1

burst

burst

AEP (t)synth

0 600

Time (ms)

speech stimulus

200 400

VOT

Figure 5.3: The signal AEP synth is constructed by additive superimposing of

two cortical responses to tone bursts AEP burst. One response AEP burst

starts at time t0 = 0, the other is delayed with the VOT of the regardedspeech stimulus.

In the time interval of the VOT the term AEP burst(t − V OT ) in Eq. 5.2is undefined (Fig. 5.3), and thus zero padded. The parameters k1 and k2

are individual weightings of the respective basic modules AEP burst. Thevalues of the parameters k1 and k2 were determined with respect to theoptimal match of AEP synth with the corresponding AEP speech. As optimalmatch of the two waveforms, the approximation of their peaks in bothlatencies and amplitudes was defined, as depicted in Fig. 5.4. This objectivewas achieved by a 2-step optimization procedure, generating the optimalparameter values k∗

1 and k∗2 as final result.


Latency

Am

plit

ude

AEPspeech

AEPsynth

Pairs ofcorresponding

peaks

Figure 5.4: Objective of the parameter optimization: Approximation of the

peaks of AEP synth to the corresponding peaks of AEP speech in both la-tency and amplitude.

In a first step, the morphology of AEP synth was fitted coarsely as a require-ment for the automatic peak detection. The coarse fitting was performedby minimizing the power of the error signal, i.e. the difference signal outof AEP speech(t) and AEP synth(t):

mink1,k2

||AEP speech(t) − AEP synth(t)||22. (5.3)

Gauss-Newton algorithm [98] was applied for the determination of k1 andk2 yielding the minimal error in signal power of Eq. 5.3. These param-eter values served as start values for the second step, where the actualobjective function was optimized, i.e. minimizing the distances of the Ncorresponding peaks. Therefore, both latencies lsynth

n and amplitudes asynthn

of the n = 1, . . . , N peaks of AEP synth were subtracted from lspeechn and

aspeechn of the peaks of AEP speech:

mink1,k2

1

N||(l

speech1 − lsynth

1

l0,aspeech

1 − asynth1

a0,1, . . . ,

lspeechN − lsynth

N

l0,aspeech

N − asynthN

a0,N

)||22.(5.4)

Dividing by N , the output argument is normalized for later comparisonby the number of peaks N , which varied across the stimuli. l0 and a0,n

5.3 Results 79

are scaling factors, in order to adapt the different dimensions ms and µVof peak latencies and amplitudes. The scaling factor l0 was searched ina 50 ms time interval, which is approx. half the distance between N1and P2, a0,n was searched within half the amplitude of the consideredpeak. Combining l0 = 10, 20, . . . , 50 ms and a0,n = 0.1, 0.2, . . . , 0.5 · aspeech

n

yielded fair balance between both directions (latency and amplitude) for

l0 = 20 ms, a0,n = 0.2 · aspeechn . (5.5)

For minimizing Eq. 5.4, Gauss-Newton algorithm was applied. For theweighting parameters k1 and k2 two constraints were defined:

• For electrophysiological reasons, negative values were not allowed fork1 and k2.

• Since stimulus Ei immediately begins with a vowel, in this case k1 = 0was set in Eq. 5.2 and the optimization procedure is conducted fork2 only.

5.3 Results

5.3.1 Cortical Responses

Preliminary one-way ANOVAs, which analyzed interpeak amplitudes, re-vealed significant amplitude differences between the electrode sites for allapplied stimulus types (p < 0.001, F26,162 = 3.3, . . . , 8.6). SNK post hocanalysis revealed that with all stimuli, the greatest amplitudes were elicitedin the central area of the scalp (i.e., channels C3, Cz, C4) for both, N1and P2 amplitude. Hence, the following illustrations and statistics arerestricted to channel Cz.

Fig. 5.5 shows the grand mean waveforms of the cortical responses to pre-sented stimulus types. The predominant components of the burst elicitedpotential were N1 and P2 (Fig. 5.5a, labeled with circles). Across theparticipants, N1 latency and amplitude averaged out (109±5) ms and(−5.8 ± 1.6) µV , respectively. P2 peak resided at the point (179±15)ms with an amplitude of (5.5±2.2) µV .


Morphologies of the speech-evoked potentials were more complex and dif-fered clearly across the stimuli with different numbers of detected peaksN = 2, N = 4 (Fig. 5.5b). Therefore, to avoid confusion, all peaks aredenoted in terms of latency. For instance, P174 would refer to a positivepeak occurring 174 ms post-stimulus onset. Speech-evoked potentials ofthe stimuli Ei, Bett, and Dieb exhibited N = 2 detected peaks, the stimuliPult, and Tau evoked N = 4 peaks. In contrast to the speech-evoked po-tentials, the cortical responses to the various noise sounds are more similarto each other in morphology, see Fig. 5.5c.

AEPs’ N1 latencies and interpeak amplitudes are listed in Tab. 5.1 in termsof mean and standard deviation across the participants. Remarkably, noisesound elicited AEPs (as indicated by N1 latency) are consistently earlier(mean 103 ± 8 ms) than the corresponding speech-elicited AEPs (mean113 ± 7 ms). While interpeak amplitudes (indicating AEP power) varyacross the stimuli, corresponding speech–noise pairs elicit similar interpeakamplitudes.

Table 5.1: Mean ± standard deviation of the N1 latency (in ms) and in-terpeak amplitude (in µV ) of speech and noise sound evoked potentialsat channel Cz.

Ei Bett Dieb Pult TauSpeech 106±4 119±8 120±5 107±7 119±15

N1 Lat. (ms)Noise 95±8 111±7 108±7 97±7 92±13Speech 7.8±2.5 5.1±1.6 8.9±2.7 4.3±1.6 1.9±0.7

Int. Ampl. (µV )Noise 6.2±2.0 4.6±2.1 6.7±2.1 4.5±1.5 3.9±1.9

For the analysis of hemispheric asymmetries, a paired t–test was conductedwith interpeak amplitudes of opposite channel pairs T3 ↔ T4, FC5 ↔ FC6and C3 ↔ C4, continuing from lateral to central. The mean interpeak am-plitude of T3 was significantly smaller than that of T4 for the speech stimulibut not for the noise stimuli, see t-values in Tab. 5.2. While interpeak am-plitudes at the positions FC5 and FC6 did not differ, an inversion fromright to left hemispheric predominance occurred at the central positionsC3, C4 for the speech stimuli (C3 > C4, see Tab. 5.2). Exemplarily, cor-tical responses to Dieb and DiebN across the entire scalp are depicted in

5.3 Results 81A

mp

litu

de

(µ

V)

Ei5

0

-5

5

0

-5

5

0

-5

5

0

-5

Bett

Dieb

Pult

Time (ms)0 100 200 300 400 500

5

0

-5

5

0

-5

Am

plit

ud

e (

µV

)

5

0

-5

5

0

-5

5

0

-5

5

0

-5

5

0

-5

N106

P190

N106

N120

N120

P186

P208

N194

P160P260

N114N220

P174 P292

(a)

(b)

Burst

N106

P174

N92

P172

N97

P160

N106

P190

N108

P190

N95

P177

TauN

Am

plit

ude (

µV

)

BettN

Tau

EiN

PultN

DiebN

(c)

Time (ms)0 100 200 300 400 500

Time (ms)0 100 200 300 400 500

Figure 5.5: Time courses of the presented stimuli: a) tone bursts, b) sylla-bles, c) noise sounds and their grand mean cortical responses at channelCz. Detected peaks are labeled with circles and denoted in terms of theiroccurrence.


Fig. 5.6, in order to illustrate the hemispheric asymmetries in the speech-evoked potentials.

Table 5.2: Mean interpeak amplitudes (in µV ) and t-values for the compar-ison of selected electrode pairs T3↔T4, FC5↔FC6, C3↔C4.

T3 T4 t FC5 FC6 t C3 C4 tSpeech 2.3(1.2) 3.3(1.2) -5.7*** 4.8(1.1) 4.8(1.1) 0.0 5.3(1.3) 4.9(1.3) 2.9**Noise 2.2(1.1) 2.2(1.3) -0.5 3.9(1.1) 3.8(1.2) 0.7 4.0(1.2) 4.0(1.3) 0.5Standard deviations are in parentheses. Significance: **p<0.01, ***p<0.001

5.3 Results 83

600

Time (ms)5µV

5µV

300

CzC3T3 C4 T4

Fz

F3

F7

F4

F8

Fp1 Fp2

Pz

O1 O2

P3

P7

P4

P8

FC1 FC2FC6FC5

CP1 CP2CP5 CP6

Response to speechResponse to noise

Figure 5.6: Scalp distribution of the grand mean AEP in response to Dieb(solid) and DiebN (dashed). Hemispheric asymmetries are observablewith Dieb comparing the channels T3↔T4 and C3↔C4.

5.3.2 Speech–Noise Pairs

Time intervals, where cortical responses to speech exhibit distinctly greaternegativities than those to noise sounds according to the H1-method aredepicted in Fig. 5.7 as blocks of black color AEP speech < AEP noise. In caseof significant AEP speech > AEP noise, gray blocks are used. According toFigs. 5.7a–e none of the speech–noise pair responses differs strongly during


the initial 50 ms. The highest accumulations of intervals of significantnegative differences occur in the time window of 100–300 ms after stimulusonset.

Su

bje

ct

Ei - Ei Bett - Bett Dieb - Dieb Pult - PultN

21

0 200 400

34567

600

Time (ms)

N NN

(a) (b) (c) (d)

Tau - TauN

(e)

Time (ms) Time (ms) Time (ms) Time (ms)

21

0 200 400

34567

600

21

0 200 400

34567

600

21

0 200 400

34567

600

21

0 200 400

34567

600

Figure 5.7: Intervals of significant differences between the responses tospeech and corresponding noise stimuli according to the H1-methodfor each subject (black areas: AEP speech < AEP noise, gray areas:AEP speech > AEP noise).

Fig. 5.8 shows the grand mean AEP waveforms of the speech (dashed lines)and noise stimuli (dotted lines) as well as the difference signals (solid lines)AEP speech−AEP noise. In the difference signal a negative component couldbe observed in a latency range of approximately (140±15) ms. For eachspeech–noise pair, one-tailed t–tests (subjects x channels) showed that theoccurring negative component was significantly smaller than zero. Laten-cies and amplitudes of the negative component in the difference signal aredescribed in detail in Tab. 5.3. Ei elicits the smallest latency, latencies inresponse to Bett, Dieb, and Tau are similar and Pult elicits the largestlatency.

Table 5.3: Mean latencies (in ms) and amplitudes (in µV ) of the negativecomponent in the difference signal out of the speech–noise pairs averagedover all channels.

Speech–noise pair Ei Bett Dieb Pult TauLatency (in ms) 123(12) 138(12) 142(11) 160(17) 146(14)

Amplitude (in µV ) -1.36(1.08) -0.78(1.12) -2.46(1.44) -1.81(1.11) -1.40(0.91)Standard deviations are in parentheses.

5.3 Results 85

Pult - PultN

Dieb - DiebN

Bett - BettN

Ei - EiN

AEPs to speechAEPs to noiseDifference signal

0 100 200 300 400 500

Time (ms)

5

0

-5

5

0

-5

5

0

-5

5

0

-5

Am

plit

ude (

µV

)

5

0

-5

Tau - TauN

Figure 5.8: Grand mean waveforms at channel Cz of speech-evoked re-sponses (dashed), noise evoked responses (dotted) and the difference sig-nal (solid).


Fig. 5.9 shows the VOT of the applied speech stimuli plotted against thepeak latency of the negative component in the difference signal. A promi-nent correlation of VOT and peak latency is observable. Spearman rankcorrelation analysis revealed that the latencies were significantly positivelycorrelated with VOT (ρ = 0.66, p < 0.01).

100

110

120

130

140

150

160

170

180

VOT (ms)

Late

ncy (

ms)

0 35 60 80

Ei

Bett

Dieb

Pult

105

Tau

Figure 5.9: Box-whisker plot of VOTs of the various speech stimuli againstthe latencies of the negative components in the difference signal of thecortical responses. Dashed lines within the boxes display the median, theedges of the boxes display the quartiles. Whiskers indicate maximal andminimal latencies, unless outliers ”+” occur (whiskers span 1.5 timesthe inter-quartile range).

5.3 Results 87

5.3.3 Fitting Results

Fitting of Grand Mean Waveforms

Fig. 5.10 shows the fitting of AEP synth (dashed) to the grand mean wave-forms of AEP speech (solid) for the different speech stimuli. In order toassess the overall fitting quality, the correlation coefficients r out of re-constructed AEP synth and AEP speech in the time interval 0–600 ms weredetermined. The coefficients r ranged from 77% at stimulus Tau to 96%at stimulus Ei. Obviously, pairs of corresponding peaks are successfullyapproximated in both latency and amplitude (Fig. 5.10).

Ei

-4

0 100 200 300 400 500 600

Time (ms)

-2

0

2

4

Am

plit

ude (

µV

)

(a)Bett

(b)

Pult(d)

AEPspeech

AEPsynth

r=96% r=79%

r=89%

Dieb(c)

r=79%

Tau(e)

r=77%-4

-2

0

2

4

Am

plit

ude (

µV

)

0 100 200 300 400 500 600

Time (ms)

0 100 200 300 400 500 600

Time (ms)

0 100 200 300 400 500 600

Time (ms)

0 100 200 300 400 500 600

Time (ms)

-4

-2

0

2

4

Am

plit

ude (

µV

)

-4

-2

0

2

4

Am

plit

ude (

µV

)

-4

-2

0

2

4

Am

plit

ude (

µV

)

Figure 5.10: Grand mean AEP speech waveforms (solid) and the according

approximated AEP synth (dashed) at channel Cz are superimposed. Thecoefficient r describes the correlation of both curves. The peaks consid-ered within the optimization procedure are labeled with circles.


Fitting of Individual Subjects

Eq. 5.4 described the mean distance of the AEP synth peaks to the cor-responding peaks of AEP speech, i.e. the smaller the output argument ofEq. 5.4 the better the fitting, optimal match equals zero. This outputargument was compared between subjects and speech stimuli, in orderto assess the fitting quality on individual level. Averaged over subjects,the output argument ranged from 0.74 at stimulus Ei to 1.16 at stimulusTau. Although the values seem different for Ei and Tau, a Kruskal-Wallistest (conducted due to the small sample size and non-normality) revealedno significant differences across the stimuli (p = 0.05). Also revealed byKruskal-Wallis test, the output arguments did not differ significantly acrossthe individual subjects (p = 0.05). Hence, the fitting quality appeared in-dependently of stimuli and subjects.

Fig. 5.11 shows the deviations of AEP synth from AEP speech in peak laten-cies (transparent boxes) and amplitudes (gray boxes) as box-whisker plots.Median latency deviations ranged from 8 ms at Ei to 12 ms at Bett, me-dian amplitude deviations ranged from 0.23 µV at Dieb to 0.43 µV at Pult.The strongest spread in the peak fitting quality was observed at stimulusTau. The correlation coefficients r out of AEP synth and AEP speech of theindividual subjects are shown in Fig. 5.12 as box-whisker plots. In median,r = 68% is reached for each stimulus. Thus, altogether high fitting qual-ities were gained. According to Fig. 5.12, best correlations were achievedfor stimulus Ei. An outlier appeared at stimulus Tau, which would havematched without the physiologically motivated constraint k1, k2 ≥ 0.

5.3 Results 89

0

20

40

50

10

30

Late

ncy D

evia

tion (

ms)

Ei Bett Dieb Pult Tau

0

1.0

2.0

2.5

0.5

1.5

Am

plit

ude D

evia

tion (

µV

)

Figure 5.11: Box-whisker plot of the subjects’ fitting deviations in peak la-tency (transparent boxes) and peak amplitude (grey boxes) for each stim-ulus, nomenclature as in Fig. 5.9.

Stimulus

0

20

40

60

80

100

Ei

Co

rre

latio

n C

oe

ffic

ien

t (%

)

Bett Dieb Pult Tau

Figure 5.12: Box-whisker plot of the subjects’ fitting correlation coefficientsfor each stimulus, nomenclature as in Fig. 5.9.


Weighting parameters k∗1 and k∗

2

The optimized weighting parameters k∗1 and k∗

2 for each individual subjectare shown in Tab. 5.4 (means and standard deviations are listed bold).Parameters extracted from grand mean fitting are listed in the first line(line GM). For stimulus Ei, k∗

1 was set to zero prior to fitting, since Eiimmediately started with a vowel. Regarding the values of k∗

1 and k∗2 across

the individual subjects, an inter-individual stability can be observed withinthe distinct stimuli. The values of k∗

1 and k∗2 of the individual fittings closely

match those of the grand mean fitting. k∗1 and k∗

2 were counterbalanced(k∗

1 : k∗2 ≈ 1:1) at stimuli Bett, Pult, and Tau, and ranged around 0.30 <

k∗1, k∗

2 < 0.41 at the grand mean fitting. At stimulus Dieb, (GM: k∗1 =

0.93, k∗2 = 0.41), the relation k∗

1 : k∗2 ≈ 2:1 arose.

Table 5.4: Values of parameters k∗1 and k∗

2 of each individual subject andtheir mean ± standard deviation (bold) for each stimulus. Line GM:parameter values of the grand mean waveforms.

Ei Bett Dieb Pult TauSubject k∗

2 k∗1 k∗

2 k∗1 k∗

2 k∗1 k∗

2 k∗1 k∗

2

GM 0.81 0.41 0.30 0.93 0.41 0.34 0.37 0.31 0.311 0.79 0.40 0 0.89 0.36 0.46 0.40 0 0.302 0.68 0.50 0.40 1.11 0 0.37 0.45 0.32 0.803 0.66 0.34 0.07 0.79 0.14 0.41 0.27 0.40 0.294 0.77 0.51 0 0.65 0.35 0.08 0.37 0.20 0.365 0.82 0.19 0.31 0.72 0.91 0.26 0.20 0.38 0.096 0.78 0.59 0 1.08 0.49 0.37 0.50 0.26 0.397 0.75 0.57 0.42 0.74 1.03 0.74 0.35 0.68 0.15

mean 0.81 0.41 0.30 0.93 0.41 0.34 0.37 0.31 0.31

5.4 Discussion 91

5.4 Discussion

5.4.1 Speech- and Non-Speech-Evoked Potentials

In accordance with other studies (e.g. [33, 36, 106]) AEP amplitudes weremaximal at central electrode sites with all applied stimulus types. AEPs totone bursts exhibited the ordinary N1/P2 structure. Regarding the wave-forms of speech- and noise-elicited AEPs (see Fig. 5.5) and contrasting theinterpeak amplitudes in Tab. 5.1 (speech-elicited AEPs have higher levelsof significance) reveals that auditory processing of speech varies stronger incontrast to noise sound processing. The speech-evoked potentials distinctlyvary in the time interval of the N1/P2 complex. This means, the AEP mor-phologies were basically influenced by the features in the beginning of thepresented stimuli.

Winkler et al. (1997) demonstrated that N1 latencies evoked by spectral-pitch and missing-fundamental tones do not differ [121], which supports theview that the N1-generating mechanisms are not directly underlying pitchperception [25]. Hence, the periodicity of the presented vowels is probablynot the reason for the found variability in the speech-evoked potentials.Considering previous studies, a more plausible explanation are the variantVOTs of the presented speech stimuli, which are reflected in N1 and P2[102,104,114,122].

5.4.2 Hemispheric Asymmetries

In contrast to noise-elicited AEPs, hemispheric differences occurred withspeech-evoked AEPs in the present study. Hemispheric left-overbalancedasymmetries with speech perception are well known [123–125]. Overbal-ance to the left is primarily reported in studies with attentive designs. How-ever, focused auditory attention can selectively modulate sensory process-ing in auditory cortex and thus, affect AEPs [115]. Dehaene-Lambertz etal. (2005) demonstrated in a discrimination task that the left-hemisphericpredominance with phoneme perception also can be achieved with appro-priate non-speech stimuli [117]. Thus, specialization of left auditory cor-tex is not speech-specific but depends on rapid spectro-temporal changes.


However, in the present study, a right-hemispheric predominance was ob-servable at lateral sites, which inverted to a left-overbalance at central sites.This could be due to deviant locations in the activated auditory areas ofboth sides, resulting in different dipole orientations, which underlie AEPderivations [126].

Another explanation is a functional asymmetry of the auditory cortices.Positron emission tomography findings of Zatorre & Belin (2001) indicatedcomplementary specializations of left and right auditory cortex’ belt areas.Responses to temporal features were weighted towards the left, while re-sponses to spectral features were weighted towards the right hemisphere[127]. Thus, the spectral changes of the consonant-vowel transitions maybe the reason for the observed right hemispheric overbalance. A fortiori,since the noise stimuli, which did not exhibit spectral changes, did notevoke asymmetric responses either. Due to the periodotopic organizationof the auditory cortex [128], the neural activity elicited by the vowel, whichis characterized by periodic oscillations, may also contribute to the asym-metry.

5.4.3 Speech–Noise Pairs

Although phonetic changes were present in the speech stimuli during theentire propagation of approx. 500 ms, AEPs of speech–noise pairs differedpredominantly in the time window of the N1/P2 complex between 100and 300 ms (Fig. 5.7), reflecting distinct onset processing. For tonotopicalorganization of the auditory cortex, the latencies and amplitudes of N1and P2 are influenced by the stimulus frequency [129]. Thus, care has tobe taken with the attempt to compare AEPs in response to speech stimuliincluding low frequency vowels and potentials evoked by wideband noisestimuli. Otherwise, Jones (2006) demonstrated that N1 and P2 latenciesvary by less than 2 ms in response to noise stimuli with different frequencyranges [130] and therefore spectral effects on AEPs are small in contrastto the results of the present study.

Subtracting noise- from speech-elicited AEPs facilitated a direct compar-ison of the speech–noise pairs. Thus, the presented paradigm allows ob-serving the neural processing of spectral-acoustic and phonetic information,since differences in stimulus duration and amplitude are eliminated. The

5.4 Discussion 93

comparison revealed a significant negative component in the difference sig-nal that seems to correlate with the VOT of the presented stimulus (seeSteinschneider et al. (1999) who found that synchronized activity in theauditory cortex is time locked with consonant release and voicing onset[131]). Since the speech-evoked N1 and P2 appear somewhat later andheightened in contrast to the noise-evoked ones, the present study suggeststhat the observed negative component denotes a second N1/P2 complex,which in the waveforms is merged with the onset response and therefore isnot directly visible. This second N1/P2 complex is suggested to reflect theonset of the vowel as an acoustic event.

Ostroff et al. (1998) demonstrated that the vowel onset response is pre-served in the response to a complete syllable [114]. The present findings arein accordance with this study and others examining ACC, which suggestthat overlapping AEPs within one stimulus response are the consequence ofdifferent N1/P2 complexes reflecting the acoustic changes across the entirestimulus [34,100].

5.4.4 Fitting of the Speech-Evoked Potentials

For all presented stimuli high fitting quality of the synthetic waveform wasobtained which did not vary significantly between individual subjects. Onindividual level, pairs of corresponding peaks deviated from each otherapprox. 10 ms and 0.3 µV in latency and amplitude, respectively. This re-sulted in consistent high correlations between the synthetic and the speech-elicited waveforms on both grand mean level (77% < r < 96%) and individ-ual level (68% < mean r < 85%). Hence, speech-evoked potentials couldbe emulated successfully using the presented approach. However, on indi-vidual level few outliers occurred at the long-VOT stimuli Pult and Tau(see Fig. 5.11). In one subject, fitting of the AEP to stimulus Tau evenfailed (Fig. 5.12), due to self-imposed constraints of the optimization al-gorithm. Nevertheless, despite their comparatively complex morphologieswith N = 4 fitted peaks, remarkably good fitting results were achieved forthe long-VOT stimuli.

In the present study, the initial consonant and the following vowel are as-sumed as independent acoustic events. The successful emulation via N1/P2structures supports the conclusion that the morphologies of speech-evoked


potentials can primarily be explained as a sequence of N1/P2 complexesoccurring in succession of new acoustic events. This conclusion is in accor-dance with the results of other ACC works observing multiple event-relatedN1 responses in speech-evoked potentials [106,114].

5.4.5 Weighting Parameters of the Fitting

Remarkably, the values k∗1 and k∗

2 of the weighting parameters were coun-terbalanced for the stimuli Bett, Pult, and Tau and ranged approx. from0.3 to 0.4. For stimulus Dieb, k∗

1 was approx. twice k∗2. Except for few

outliers, k∗1 and k∗

2 were inter-individually stable. The different values ofthe weighting parameters can be interpreted as distinct intensities in theauditory processing of the vowel in contrast to the initial consonant, i.e. atBett, Pult, and Tau, both sounds elicit equal neural activity. Potentially,benchmarks concerning the unimpaired neural detection of speech soundproperties can be derived on the basis of this phenomenon.

5.4.6 Summary

Deviations in the speech-evoked potentials of central auditory impairedgroups in contrast to control groups are well known [132, 133]. Nonethe-less, AEP methods concerning auditory performance are still undeveloped,because of the strong inter-individual deviations in AEP waveforms. Thisstudy demonstrated that the auditory processing of rapid temporal changescan be assessed electrophysiologically. Advantageously, the presented ap-proach does not depend on inter-individual comparisons of AEPs confiningthe clinical applicability. Provided that patients with speech impairmentdue to central auditory processing disorders reveal modified cortical re-sponses, the presented approach could serve as diagnostic tool for phoneticdiscrimination tasks, since it reflects the neural processing of time-criticalphonetic structures of speech. However, speech-evoked potentials are em-ulated exclusively by obligatory N1/P2 responses in this study. For thereconstruction of the AEP morphology in a larger time window, furtherexo- and endogenous AEP components will be regarded in future.

6 Summary and Outlook

The measurement of electric fields on the human scalp during acousticstimulation allows the observation of neural brain activity expressing audi-tory processing [33]. These auditory evoked potentials (AEPs) can reflectdistinct layers of the auditory processing and perception [25]. While brain-stem AEPs testing the integrity of the auditory pathway are part of theclinical routine today, cortical AEPs as electrophysiological correlatives ofperception, are still a matter of neurophysiological research. However, theapplicability of cortical AEPs as tool for audiological diagnostics has al-ready been shown in many studies to date. This work contributes to theclinical utilization of cortical AEPs by verifying their performance in signif-icant audiological problems and by introducing methods for the automaticprocessing and assessment of AEP characteristics.

In Chapter 3, the auditory discrimination capabilities of preschool childrensuffering from central auditory processing disorder were examined usingthe AEP component of mismatch negativity (MMN). An approach for theautomatic identification of MMN and the extraction of its characteristicswas introduced. This approach based on the statistical analysis of singlesweeps. Hence, the subjective and error-prone visual inspection of aver-aged signals from clinical experts can be substituted. On group level, thehypothesis that CAPD children reveal substandard discrimination perfor-mance [47] could be verified. However, on the level of individual patientsthe selectivity of the MMN for CAPD diagnostics is insufficient, since au-ditory discrimination alone does not cover CAPD etiology [8]. By means ofthe discrete wavelet transform, a description of complex AEP morpholo-gies was possible by few parameters. Thus, in the examined group ofchildren the individual maturation of the auditory pathway could by clas-sified. However, it has been shown that the status of auditory developmentdoes not reflect the individual performance in auditory perception. In fu-ture, the benefit of the MMN for audiological purposes can be enhanced,

95

96 6 Summary and Outlook

by directly comparing MMN results with psychometrically assessed audi-tory functions, like auditory memory span. In case of high correlation, thesubjective method could be substituted by the objective one, and thus,the auditory function is measured independently from the cooperation ofthe patient. For the improvement of the MMN selectivity, variations inthe physical features of the stimuli and the contrast between standard anddeviant stimulus have to be considered.

Today it is assumed that deficits in temporal aspects of audition are primar-ily responsible for specific language impairment in children [54]. For thisreason, in chapter 4 we examined whether auditory temporal processes arereflected in AEPs, temporarily in normal-hearing adults. We compared cor-tical responses to time-critical stimuli with the psychometrically assessedfusion threshold which is an indicator for individual temporal resolution.It was shown that in case of a presented gap stimulus continuum, certainAEP characteristics highly correlate with the individual fusion threshold.This result is a first step towards the application of AEP for the measure-ment of the temporal resolution on individual layer. In further studies,the introduced AEP design should be applied to patients with demonstra-ble deficits in auditory temporal processing in order to verify or falsify thementioned correlation between AEP characteristic and fusion threshold forclinical populations.

In their complexity, phonetic structures of speech — which the brain has toprocess in real-time — exceed synthetic acoustic stimuli by far. In chapter5, it was examined to which extent phonetic structures are reflected inspeech-evoked potentials. In addition, it was investigated whether speech-specific neural centers contribute to the processing of speech already onauditory-sensory level. Therefore, the portion of speech-specific generatorsappearing in the evoked potentials was measured. It has been shown thaton auditory-sensory level the brain predominantly analyzes the physicalstimulus constitution, since in absence of attention the measured AEPwaveforms traced back to physical features of the input. In addition, thecomposition of speech-evoked potentials was described by event-relatedcortical ”basic modules” multiply evoked during the presentation of thestimulus. This result verified the hypothesis of the existence of the so-called acoustic-change complex (ACC) [100]. Furthermore, by means of thepresented approach based on an optimized fitting of the cortical responsesby obligatory AEP components, the origin of the ACC was revealed. For

97

the application in speech diagnostics, e.g. for the assessment of phoneticdiscrimination performance, further studies have to be conducted. Onthe basis of the introduced approach, it should be verified whether thecontribution of the single auditory ”basic modules” reflects the individualperceptive ability.

Altogether, cortical AEPs provide an excellent tool for the assessment ofthe human auditory perception. In future works efforts should be intensi-fied to fill the lack of selectivity for diagnostic purposes.

98 6 Summary and Outlook

Zusammenfassung und Ausblick

Die Messung elektrischer Feldveranderungen auf der Kopfoberflache wah-rend akustischer Stimulation ermoglicht die Beobachtung neuronaler Ge-hirnaktivitat als Ausdruck auditiver Verarbeitung [33]. Diese akustischevozierten Potentiale (AEP) konnen verschiedene Ebenen der auditivenVerarbeitung und Wahrnehmung abbilden [25]. Wahrend Hirnstamm-Potentiale, die die Integritat der Horbahn testen, heutzutage Teil des kli-nischen Alltags geworden sind, sind die kortikalen Potentiale nach wie vorGegenstand neurophysiologischer Forschung geblieben, obwohl ihre An-wendbarkeit in der audiologischen Diagnostik bereits in vielen Studienbelegt wurde. Diese Arbeit leistet einen Beitrag zur klinischen Nutzbar-machung kortikaler AEP durch die Uberprufung deren Leistungsfahigkeithinsichtlich signifikanter audiologischer Fragestellungen und durch die Ein-fuhrung von Methoden fur die automatische Verarbeitung und Bewertungvon AEP-Merkmalen.

In Kapitel 3 wurde die auditive Diskriminationsfahigkeit von Vorschulkin-dern, die unter einer auditiven Verarbeitungs- und Wahrnehmungsstorung(AVWS) leiden, mittels der AEP-Komponente Mismatch-Negativity (MMN)untersucht. Zudem wurde ein Verfahren fur die automatische Erkennungeiner MMN und der Extraktion ihrer Merkmale vorgestellt. Dieses Ver-fahren beruht auf der statistischen Auswertung einzelner Sweeps und kanndie subjektive und fehleranfallige ”visuelle Inspektion” gemittelter Sig-nale von klinischen Experten ersetzen. Auf Gruppenebene konnte dieHypothese, dass AVWS-Kinder unterdurchschnittliche Diskriminationslei-stungen erbringen [47], verifiziert werden. Auf Einzelpatientenebene je-doch ist die Trennscharfe der MMN fur eine AVWS-Diagnostik unzurei-chend, auch weil auditive Diskrimination alleine die Atiologie der AVWSnicht abdeckt [8]. Die diskrete Wavelet-Transformation ermoglichte eine

99

100 8 Zusammenfassung

Beschreibung komplexer AEP-Morphologien vermittels weniger Parameter.So konnte innerhalb der untersuchten Gruppen die individuelle Reifung derHorbahn klassifiziert werden. Es hat sich aber gezeigt, dass der Entwick-lungsstatus des auditiven Systems nicht die individuelle Wahrnehmungslei-stung widerspiegelt. Kunftig kann der Nutzen der MMN fur audiologischeZwecke gesteigert werden, indem MMN-Ergebnisse direkt mit psychome-trisch ermittelten auditiven Teilleistungen, wie der Hor-Merkspanne, ver-glichen werden. Im Falle einer hohen Korrelation konnte das subjektiveVerfahren durch das objektive ersetzt werden, und somit die betrachteteauditive Teilleistung unabhangig von der Mitarbeit des Patienten gemessenwerden. Um die Trennscharfe der MMN zu steigern, sollten die physika-lischen Reizeigenschaften und die Kontraste zwischen Standard- und De-viantreiz variiert werden.

Heute nimmt man an, dass vorrangig Defizite in der auditiven Zeitverar-beitung fur eine gestorte kindliche Sprachentwicklung verantwortlich sind[54]. Aus diesem Grunde wurde in Kapitel 4 — vorlaufig an normal-horenden Erwachsenen — untersucht, ob sich zeitkritische Verarbeitungs-prozesse in den AEP abbilden. Wir verglichen die kortikalen Antwortenzeitkritischer akustischer Reize mit der psychometrisch ermittelten Fu-sionsschwelle, die einen Indikator fur das individuelle zeitliche Auflosungs-vermogen darstellt. Es konnte gezeigt werden, dass im Falle eines Kontinu-ums von Lucken in den prasentierten Stimuli gewisse AEP-Charakteristikamit der individuellen Fusionsschwelle korrelieren. Dieses Ergebnis ist einerster Schritt in Richtung Anwendbarkeit der AEP fur die Bestimmung deszeitlichen Auflosungsvermogens. In weiteren Studien soll das vorgestellteAEP-Verfahren an Patienten mit nachweislichen Defiziten in der auditivenZeitverarbeitung angewendet werden, um die erwahnte Korrelation zwi-schen AEP-Merkmalen und Fusionsschwelle bei klinischen Populationenzu verifizieren/falsifizieren.

In ihrer Komplexitat ubersteigen sprachliche, phonetische Strukturen —die das Gehirn in Echtzeit verarbeiten muss — synthetische akustischeReize bei Weitem. In Kapitel 5 wurde untersucht, in welchem Ausmaß sichphonetische Strukturen in sprach-evozierten Potentialen abbilden. Zusatz-lich wurde gefragt, ob bereits auf auditorisch-sensorischer Ebene sprach-spezifische neuronale Zentren zur Verarbeitung von Sprache beitragen.Hierfur wurde der Anteil sprach-spezifischer Generatoren, die in den evo-zierten Potentialen erscheinen, bestimmt. Es wurde gezeigt, dass das

8 Zusammenfassung 101

Gehirn auf auditorisch-sensorischer Ebene vorrangig die physikalische Reiz-zusammensetzung analysiert, da in Abwesenheit von Aufmerksamkeit dieAEP-Kurven auf die physikalischen Reizeigenschaften zuruckgefuhrt wer-den konnen. Zusatzlich wurde die Zusammensetzung sprach-evozierterPotentiale durch ereignis-korrelierte ”Grundbausteine” beschrieben, diewahrend der Prasentation mehrfach evoziert werden und sich uberlagern.Dieses Ergebnis stutzt die Annahme der Existenz des sogenannten Acoustic-Change-Complexes (ACC) [100]. Außerdem wurde der Ursprung des ACCmit Hilfe eines Optimierungsverfahrens, bei dem die kortikalen Antwortendurch obligatorische Komponenten angenahert werden, enthullt. Fur dieAnwendung in der Sprachdiagnostik, d.h., fur die Bewertung phonetischerDifferenzierung, mussen weitere Studien durchgefuhrt werden. Auf derGrundlage des vorgeschlagenen Verfahrens sollte uberpruft werden, in wieweit die Gewichtung einzelner ”Grundbausteine” die individuelle Sprach-wahrnehmung reflektiert.

Insgesamt stellen die kortikalen AEP ein hervorragendes Werkzeug fur dieBewertung der auditiven Wahrnehmung dar. In zukunftigen Arbeiten soll-ten Anstrengungen unternommen werden, um den Mangel an Trennscharfefur die audiologische Diagnostik zu tilgen.

102 8 Zusammenfassung

List of Abbreviations

AC auditory cortexACC acoustic change complexAEP auditory evoked potentialsAUC area under the curveBERA brainstem electrical response audiometryCAPD central auditory processing disorderDD duration deviantDWT discrete wavelet transformEEG electroencephalogramFD frequency deviantGD gap deviantGT gap detection thresholdACC acoustic change complexHP high-pass filterID intensity deviantISI inter-stimulus intervalLDN late discriminative negativityLP low-pass filterMMN mismatch negativityMRA multiresolution algorithmOAE otoacoustic emissionsOT ordering thresholdSD standard deviationSLI specific language impairmentSNR signal-to-noise ratioSPL sound pressure levelSS standard stimulusVOT voice onset-time

103

104 List of Abbreviations

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118 Bibliography

List of Figures

2.1 The auditory pathway . . . . . . . . . . . . . . . . . . . . 102.2 Model of the auditory perception . . . . . . . . . . . . . . 142.3 Latencies of AEPs . . . . . . . . . . . . . . . . . . . . . . 152.4 Electrode sites of the 10-20 system . . . . . . . . . . . . . 172.5 Generation of neural dipoles . . . . . . . . . . . . . . . . . 192.6 Obligatory AEPs of different age groups . . . . . . . . . . 22

3.1 Test battery . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2 MMN paradigm . . . . . . . . . . . . . . . . . . . . . . . . 303.3 Subband Coder . . . . . . . . . . . . . . . . . . . . . . . . 333.4 Coiflet2 wavelet . . . . . . . . . . . . . . . . . . . . . . . . 333.5 DWT reconstruction . . . . . . . . . . . . . . . . . . . . . 343.6 H1-Method . . . . . . . . . . . . . . . . . . . . . . . . . . 363.7 AEPs of the deviants . . . . . . . . . . . . . . . . . . . . . 403.8 P1 classes after cluster analysis . . . . . . . . . . . . . . . 423.9 Grand mean MMNs . . . . . . . . . . . . . . . . . . . . . . 433.10 SNR of the deviant stimulus AEPs . . . . . . . . . . . . . 443.11 Negativities revealed by H1-method . . . . . . . . . . . . . 453.12 Scalp distribution of the difference wave . . . . . . . . . . 47

4.1 Gap stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . 574.2 Model function . . . . . . . . . . . . . . . . . . . . . . . . 604.3 AEPs waveforms to gap-stimuli . . . . . . . . . . . . . . . 614.4 Boxplot of latencies . . . . . . . . . . . . . . . . . . . . . . 624.5 N1-P2 interpeak amplitudes . . . . . . . . . . . . . . . . . 644.6 Gradients of the P2 latencies . . . . . . . . . . . . . . . . . 65

5.1 Noise stimulus generation . . . . . . . . . . . . . . . . . . 735.2 Spectrograms of the applied stimuli . . . . . . . . . . . . . 745.3 Superimposing of N1/P2 . . . . . . . . . . . . . . . . . . . 775.4 Target of the optimization . . . . . . . . . . . . . . . . . . 78

119

120 List of Figures

5.5 AEPs of the adults . . . . . . . . . . . . . . . . . . . . . . 815.6 AEP scalp distribution to a speech–noise pair . . . . . . . 835.7 Intervals of differing speech–noise responses . . . . . . . . 845.8 Difference waveforms of speech − noise responses . . . . . 855.9 Latency of the negative component against VOT . . . . . 865.10 Fitting of the grand mean AEPs . . . . . . . . . . . . . . . 875.11 Fitting quality . . . . . . . . . . . . . . . . . . . . . . . . . 895.12 Correlation between original and reconstruction . . . . . . 89

List of Tables

3.1 P1/N2 latencies and amplitudes . . . . . . . . . . . . . . . 413.2 Crosstab P1 morphology in s/c . . . . . . . . . . . . . . . 423.3 MMN amplitudes . . . . . . . . . . . . . . . . . . . . . . . 46

4.1 Gap Detection Protocol . . . . . . . . . . . . . . . . . . . 594.2 Amplitudes as gap length function . . . . . . . . . . . . . . 63

5.1 N1 latency and interpeak amplitude . . . . . . . . . . . . . 805.2 Hemispheric asymmetries . . . . . . . . . . . . . . . . . . . 825.3 Negative Component . . . . . . . . . . . . . . . . . . . . . 845.4 Weighting parameters . . . . . . . . . . . . . . . . . . . . . 90

121

122 List of Tables

Lebenslauf

Name: BurgerVorname: Martin RudolfGeburtsdatum: 18.11.1976Geburtsort: ErlangenStaatsangehorigkeit: deutschFamilienstand: ledig, keine Kinder

1983 - 1987 Grundschule Weisendorf1987 - 1996 Gymnasium Herzogenaurach09/1996 - 10/1997 Zivildienst11/1997 - 06/2003 Elektrotechnikstudium an der

Friedrich-Alexander-Universitat Erlangen-Nurnberg01/2003 - 06/2003 Diplomarbeit am Fraunhofer-Institut

IIS in ErlangenThema: ”Drahtlose Kommunikation unter µCLinux”

07/2003 - 06/2004 Hard- und Software-Entwickler beiMotron Steuersysteme GmbH in Heßdorf

seit 07/2004 Wissenschaftlicher Mitarbeiter in derAbteilung fur Phoniatrie und Padaudiologiedes Universitatsklinikums Erlangen

123

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