marco congedo, phd france telecom r&d
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Conjunct COST B27 and SAN Scientific Meeting, Swansea, UK, 16-18 September 2006. Classification of Movement Intention by Spatially Filtered Electromagnetic Inverse Solutions. Marco Congedo, PhD France Telecom R&D. [email protected]. Introduction. What is a BCI?. - PowerPoint PPT PresentationTRANSCRIPT
Marco Congedo, PhD France Telecom R&D
Classification of Movement Classification of Movement Intention Intention by Spatially Filtered Electromagnetic by Spatially Filtered Electromagnetic Inverse SolutionsInverse Solutions
[email protected]@Gmail.com
Conjunct COST B27 and SAN Scientific Meeting,Swansea, UK, 16-18 September 2006
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Introduction
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What is a BCI?A BCI is a system that allows humans
to transmit bits of information without making use of any motor activity.
This is achieved by detection and classification of discrete brain events.
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Peer-Rewiewed Articles on "Brain-Computer Interface" (Source: PUBMED)
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60
80
100
120
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1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Year
Nu
mb
er o
f A
rtic
les
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Domains of Applications• Motor Handicap
World Human
Input (Sensory)
Output (Motor)
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"Aware Chair"(Georgia State University)
Text Editor(Helsinki University of Technology)
Examples of Current Applications for Motor Handicap
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Domains of Applications• Motor Handicap
• Human-Machine Interface• New Interfaces• Detection of User's Intention (Video-Games, TeleInteraction)
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Domains of Applications• Motor Handicap
• Human-Machine Interface
• Virtual Reality
• New Interfaces• Detection of User's Intention (Video-Games, TeleInteraction)
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Navigation in a Virtual Environment via a Head Mounted Display and a BCI
(University of Graz)
Example of Application of BCI for Virtual Reality
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Domains of Applications• Motor Handicap
• Human-Machine Interface
• Virtual reality• Robotics
• New Interfaces• Video-Games• Detection of User's Intention
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Implantation of MicroElettrodes
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Implantation of MicroElettrodes
Advantages:• Bypass the low-pass filter enforced by the cranial bones• Small Neuronal Population Recording (High Spatial Resolution)• 24h Data Availability
Disadvantages:• Invasive
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Method
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The Motor Cortex and the detection of Movement Intention
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The Motor "Homunculus"
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pyramidal cell
Section of aCortical Gyrus
Cerebral Cortex
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Pyramical Cells of the Motor Cortex
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Subjects and Procedures• Subject: one non-clinical subject during a self-paced key pressing task.
• Task: press with the index and little fingers keys using either the left or right hand, in a self-paced timing and self-chosen order.
• Protocol: three sessions of six minutes each, with a few minutes of break between sessions.
- Epochs of 500 ms were extracted ending 130 ms before the key press.
- The epochs were divided in a training set and a test set (316 and 100).
• EEG Data: BCI Competition 2003, Data-Set IV
(Blankertz et al, 2004)
- EEG was acquired at 28 leads (F3, F1, Fz, F2, F4, FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, CP5, CP3, CP1, CPz, CP2, CP4, CP6, O1, O2) with a 1000 Hz sampling rate.
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Examples of EEG trial related to Movement Intention(from -630 ms. to -130ms. before movement onset)
Left Finger Right Finger
-630 ms -130 ms Periodogram AutoCorrelation
Fron
tal
Site
sO
ccipita
l S
ites
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Data Processing (Schematic Representation)
Band-Pass Filtering
Projection on the Beamspace(Spatial Filtering)
Source Power EstimationIn the Regions of Interest
(sLORETA)
Classification
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Band-Pass Filtering
T-tests of Left vs. Right Finger Movement Intention(N= 159 Left Fingers trials + 157 Right Fingers trials.
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Band-Pass Filtering
Threshold ofsignificance
minima
maxima
(maxima – minima)/2
Maximal and minimal absolute t-statistic across the volume for each frequency bin and their relation with the threshold of significance
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Spatial Filtering (Common Spatial Pattern)
max ; maxT T
L RT T
R L
a b
a V a b V ba V a b Vb
Problem:
Solution:First and last d vectors of the Joint Diagonalizer ofLV and
RV
T
TL
TR
F V F I
F V F W
F V F I W
where I is the identity matrix, VΣ = VL+VR,W=diag(W1≥W2≥…≥WN-1) andI-W=diag(1-W1≤1-W2≤…≤1-WN-1).
satisfying:
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sLORETA Source Power of the Filter Spatial Patterns
1
2
3
4
5
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24
25
26
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The 28 scalp coefficients are given as the 27 columns of T G F
…
…
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Actual Filter Employed
27 26R F f f
where df is the unith norm dth column vector of F.
1 2L F f f Filter for Left Motor Cortex
Filter for Left Motor Cortex
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sLORETA Source Power Estimation
TL L Ltr H VH T T T
L L L L L L Ltr H F F VF F H
T T TR R R R R R Rtr H F F VF F H
Unfiltered sLORETA Filtered sLORETA
LEFT
Moto
r C
orte
x
RIG
HT
Moto
r C
orte
x
TR R Rtr H VH
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Filtered Source power of Left and Right finger movement intention grand average training trials
Legend: R=Right; L=Left; A=Anterior; P=Posterior; S=Superior; I=Inferior.
Left
trials
Rig
ht
trials
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Classification
classify trial as finger movement intention if
classify trial as finger movement intention if
L R
L R
left
right
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Right Finger Movement Intention Trials
Left Finger Movement Intention Trials
Training Set (N=316) Test Set (N=100)
Results
Ou
r M
eth
od
Un
filte
red
sLO
RE
TA
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Discussion
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• The Classifier is Untrained
Advantages of the Method
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• The Classifier is Untrained
Advantages of the Method
• Adapt to Invividual Characteristics
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• The Classifier is Untrained
Advantages of the Method
• Adapt to Invividual Characteristics• Processing Speed
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• The Classifier is Untrained
Advantages of the Method
• Adapt to Invividual Characteristics• Processing Speed
• Non Invasiveness
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End…
AcknowledgmentsThis Research has been partially funded by the French National Research Agency within the project Open-ViBE (Open Platform for Virtual Brain Environments), and by Nova Tech EEG, Inc., Knoxville, TN.
ReferenceCongedo M., Lotte, F, Lécuyer, A. (2006),
Classification of Movement Intention by Spatially Filtered Electromagnetic Inverse Solutions, Physics in Medicine and Biology, 51, 1971-1989.
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