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Assistant Professor Yakup KUTLU

Orthogonal Extreme Learning Machine Based P300 Visual Event-Related BCI

Yakup KUTLU1∗, Apdullah YAYIK2, Esen YILDIRIM1, Serdar YILDIRIM 1

1 Department of Computer Engineering İskenderun Technical University

2 Department of Informatics Mustafa Kemal University

[email protected] November 2015

11

22𝑡ℎ International Conferance on Neural Information Process

İ𝑠𝑘𝑒𝑛𝑑𝑒𝑟𝑢𝑛


Problem Definition

What is Brain Computer Interface (BCI)Definition

BCI Paradigms (P300, SSVEP … etc.)

Database EFL Group BCI Database Description

Feature ModelMulti Order Difference Plot (MoDP)

Classifiers ModelExtreme Learning Machine (ELM)

Novelty within the ELM (QR-ELM)

Results

Contents

DatabaseFeature ModelClassifiers ModelResults

ProblemWhat is BCI


Problem Definition


Predicting considerated visual objects that are vital for mounting life via non-invasive EEG signal.

EEG P300 paradigm.

Fast acquiring feature model.Especially Time domain signals

Robust, non-iterative and fast classsifier model.Advancing Extreme Learning Machine learning kernel using linear algebra

ProblemWhat is BCI


What is Brain Computer Interface

DefinitionDefinition

Interdisiplinary communication system that allows to act on environment by using onlybrain-activity, without using peripheral nerves and muscles.

DefinitionInvasive, non-invasiveEEG

ı


Mostly non-invasive technique is preferred (EEG).

ProblemWhat is BCI


EFL Group BCI Database

Data Collecting ScenarioData Collecting Scenario

ScenarioProperties

ı


Presentation protocolSubjects were asked to count how many times a prescribed image was flashed in silence.

On the screen images in figure is flashed and a warning tone was given

The arrangement of flashes was block-randomized (after six flashes each image was flashed once, after twelve flashes each image was flashed twice, etc...). The number of blocks was chosen randomly between 20 and 25.

After each run subjects were asked what their counting result was. This was done in order to detect performance of the subjects


EFL Group BCI Database

PropertiesProperties

ScenarioProperties

ı


2048 Hz. Sampling frequency

32 channel location (10-20 IS).

Biosemi Active Two amplifier.

4 session image presentation is applied for 8 subjects (4 disabled, 4 healthy).

1 session includes 6 recordings.


Feature ModelMulti-Order Difference Plot(MoDP)Scattering of consecutive difference values with different degrees Multi-Order Difference Plot(MoDP)Scattering of consecutive difference values with different degrees


Scattering of consecutive difference Analytically determining

ididid

idiidi

yyxd

RyRxd

)1()1)(1()1(1)1)(i-(d

1

, x1

,1

11 ii Rx ii Ry 1

iii xxx 1)1(12 iii yyy 1)1(12



Main function:

d=1

d=2

d=3

d=4

Output:


-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1 4

32

3-3-3-1 6-5-1-0 1-1-1-0 6-5-3-0

46

5

3

0

Feature Model

1

3

2

MoDP-Analytically determining values in specified regions MoDP-Analytically determining values in specified regions


Scattering of consecutive difference Analytically determining

Data pairs are normalized [-1,+1]

16 quadrands in 4 circles aredetermined

Diameters are 0.25, 0.50, 0.75 and 1,00

Data in quadrands arecounted via euclideandistance


Classifier ModelExtreme Learning Machine (ELM)Extreme Learning Machine (ELM)


ELM is single layer neural network that has random nodes with random and fixed weightsand learning capacity using Moore Pensore pseudoinverse conditions

G.-B. Huang, et al., “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, pp. 489-501,2006.

Extreme Learning Machine Novelty within the ELM (QR-ELM)


Extreme Learning Machine (ELM)

,).(1

N

i

ijij bxwgHHidden layer output :

N

i

ijii

N

i

jiij bxwgxgT11

).()(

ijj HT Output layer output :

TH Linear equation

NxNNNN

NN

bxwgbxwg

bxwgbxwg

).().(

).().(

111

1111

T

N

xmN

1

T

N

Nxm

t

t

1

TH MoorePensore solutionHHHH

HHHH T)( HHHH

HHHH T )(



THHH TT 1)( Least Square Solution

Pseudoinverse Solution Singular Value Decomposition


Classifier ModelQR-ELMQR-ELM



QRA

orthogonal matrix and lower triangular matrixQ HQR Decomposition

Gram Schmidt

HouseHolder Transform

Givens TransformTQRQRQR TT )()))(( 1

TQRQRQR TTTT 1)(

)( IQQT

TQRRR TTT 1)(

TQRRR TTT 1 )( IRR TT

TQR T\

THHH TT 1)(


Results


ELM (1), hhQRELM (2) and mgsQRELM (3) classifiers with 1st DP(a), 2ndDP (b), 3rd DP (c) and 4th DP (d) features,train and test accuracies with iterative neuron number.


Results


TABLE 1. CLASSIFIERS' ACCURACY RESULTS

Classifier Feature Accuracy (%) NeuronTime

(s)

ELM

1st DP 91,631 78 0,266

2nd DP 97,894 24 0,016

3rd DP 97,894 100 0,027

4th DP 95,263 20 0,014

hhQRELM

1st DP 91,131 47 0,014

2nd DP 97,368 35 0,012

3rd DP 98,421 64 0,031

4th DP 95,263 57 0,009

mgsQRELM

1st DP 92,684 47 0,017

2nd DP 97,368 35 0,009

3rd DP 98,421 64 0,023

4th DP 95,263 57 0,018

MLP

1st DP 73,500 30-20-20-20 0,554

2nd DP 68,710 30-20-20-21 0,512

3rd DP 68,763 30-20-20-22 0,536

4th DP 79,500 30-20-20-23 0,561

SVM

1st DP 87,484

2nd DP 92,146

3rd DP 92,670

4th DP 93,193

HouseHolder QR-ELM is is average 17,4 times fasterthan MLP.

HouseHolder QRELM classifier with 64 neuron reaches98,421% general accuracy.

Except MLP classifier, 3rd DP (difference plot) featureshas higher accuracy results than others.


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QUESTIONS

Yakup KUTLU

Department of Computer Engineering İskenderun Technical University

[email protected]

09 November 2015

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