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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
Assistant Professor Yakup KUTLU
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
Assistant Professor Yakup KUTLU
Problem Definition
DatabaseFeature ModelClassifiers ModelResults
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
Assistant Professor Yakup KUTLU
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
DatabaseFeature ModelClassifiers ModelResults
Mostly non-invasive technique is preferred (EEG).
ProblemWhat is BCI
Assistant Professor Yakup KUTLU
EFL Group BCI Database
Data Collecting ScenarioData Collecting Scenario
ScenarioProperties
ı
DatabaseFeature ModelClassifiers ModelResults
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
Assistant Professor Yakup KUTLU
EFL Group BCI Database
PropertiesProperties
ScenarioProperties
ı
DatabaseFeature ModelClassifiers ModelResults
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.
Assistant Professor Yakup KUTLU
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
DatabaseFeature ModelClassifiers ModelResults
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
iii xxx 2)1(23 iii yyy 2)1(23
iii xxx 3)1(34 iii yyy 3)1(34
Main function:
d=1
d=2
d=3
d=4
Output:
Assistant Professor Yakup KUTLU
-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
DatabaseFeature ModelClassifiers ModelResults
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
Assistant Professor Yakup KUTLU
Classifier ModelExtreme Learning Machine (ELM)Extreme Learning Machine (ELM)
DatabaseFeature ModelClassifiers ModelResults
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)
Assistant Professor Yakup KUTLU
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 )(
DatabaseFeature ModelClassifiers ModelResults
Extreme Learning Machine Novelty within the ELM (QR-ELM)
THHH TT 1)( Least Square Solution
Pseudoinverse Solution Singular Value Decomposition
Assistant Professor Yakup KUTLU
Classifier ModelQR-ELMQR-ELM
DatabaseFeature ModelClassifiers ModelResults
Extreme Learning Machine Novelty within the ELM (QR-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)(
Assistant Professor Yakup KUTLU
Results
DatabaseFeature ModelClassifiers ModelResults
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.
Assistant Professor Yakup KUTLU
Results
DatabaseFeature ModelClassifiers ModelResults
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.
Assistant Professor Yakup KUTLU
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QUESTIONS
Yakup KUTLU
Department of Computer Engineering İskenderun Technical University
09 November 2015