SVM. Research on ERPs feature extraction and classification

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1 Journal of Fuzhou University Natural Science Edition Vol 39 No 1 Feb 2011 DOI CNKI /N ERPs ERPs SVM ERPs SVM 90% ERPs SVM TP391 A Research on ERPs feature extraction and classification WU Ming HUANG Zhi - hua College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian China Abstract The paper presents a novel adaptive feature extraction method based on Kolmogrov Smirnov test K - S test which could determine if independent random samples are drawn from the same underlying continuous population The method enables to extract the only useful discriminatory information as feature for classification and it is adaptive for the varied brain activities SVM having good performance for pattern classification is used to construct classifier based on the feature attributes extracted The experimental results show that the adaptive feature extraction method could effectively extract the discriminatory information as classification feature and greatly reduce the dimension Furthermore combination of the adaptive feature extraction method and SVM could classify ERPs of brain activities Its classification accuracy is over 90% Keywords ERPs SVM classification feature extraction event - related potentials ERPs 1 ERPs 2 ERPs ERPs ERPs 20 μv 150 μv EEG ERPs ERPs 3 independent component analysis ICA principal component analysis PCA FFT WT autoregression AR ERPs radial basis function neural betwork RBFNN back propagation neural network BPNN Kolmogrov - Smirnov test K - S test ERPs E - mail hzh@ fzu edu cn XQ H0013

2 1 ERPs 55 support vector machines SVM 6 SVM ERPs ERPs 3% ~ 7% SVM 90% ERPs 1 2ERPs EEG 3 EEG 0 7 C t = S t + E t C t S t ERPs E t EEG 1 C m n = S m n + E m n 2 m n Hz 500 ms C m n C i j i j 1 i m 1 j n ERPs S i j S^ i j = C i 珔 j = 1 C r r k i j 1 i m 1 j n 3 k = 1 r C k i j k S^ i j r ERPs r - ERPs Lilliefors test 8 C i j 1 2 ERPs K - S test S m n C i j K - S test C i j C i j ERPs S m n S i j S i j ERPs 1 0 Z m n Z m n Z m n Z i j = 0 p a i j = p b i j 1 i m 1 j n 4 1 p a i j p b i j p a i j p b i j A B C i j K - S test A C i j B C i j Z i j = 0 Z i j = 1 S i j S * m n = S m n * Z m n 5 5 * S * i j = S i j * Z i j S * m n SVM SVM VAPNIK 6 11 Mercer SVM 1

3 / w Φ x i R d Φ x i R h Φ x i T Φ x j Mercer K x y Φ 11 Φ x i Φ x i Sigmoid SVM ξ C SVM wt w + C n min ξ i w b ξ i = 1 s t y i w T Φ x i + b ξ i 0 ξ i 0 w ξ Φ x i R h y i i = 1 n 6 max e T α - 1 α 2 αt Qα s t y T α = 0 0 a i C e T = Q ij = y i y j K x i x j 7 a i 7 a * i i = 1 sv 6 7 f x = sgn n * y i k x i x + b } = sgn a * * i y i K x i x + b } 8 8 a * i 7 b * a * i a * i 0 0 a * i SV Fig 1 1 Events and durations composing single - trial of data acquisition

4 1 ERPs 57 D1 D2 R1 R Neuro Scan Hz 3 2 Neuro Scan ms 499 ms 700 ms ms ms 7 C m n D1 D2 R1 R2 4 C m n Z m n 1 2 ERPs S m n Z m n ERPs ERPs 4 D1 D2 R1 R2 6 D1 D2 D1 R1 D1 R2 D2 R1 D2 R2 R1 R2 ERPs 8 ERPs 8 - ERPs 14 h h r r - ERPs 15 ERPs 8 - ERPs 8 - ERPs max 1 min - 1 x 1-2* max - x / max - min max min x 1-2* max' - x / max' - min' max' min' SVM k x y = exp - x - y 2 /σ 2 = exp - γ* x - y 2 5 C γ C C γ sv a * i f x = sgn a * 2 i y i * exp - γ* x - x i } sv % A D2 R2 C m n K - S test Z m n 9

5 58 39 Z i j 1 2 i - 1 j 2 2 ERPs K - S test 1 Z m n 512 3% ~ 7% SVM 90% SVM ERPs Fig 2 2 A D2 R2 - Feature and determination matrix under brainwave states D2 and R2 of tester A Tab 1 1 K - S Dimension of features extracted based on K - S test original data space is A B C D E F D1 D D1 R D1 R D2 R D2 R R1 R Tab 2 Classification accuracy of two different brainwave states % P A P B P C P D P E P F D1 D D1 R D1 R D2 R D2 R R1 R

6 1 ERPs 59 4 K - S test SVM ERPs 1 J J Makeig S Debener S Onton J et al Mining event - related brain dynamics J TRENDS in Cognitive Sciences M MATLAB M Vapnik V N The nature of statistical learning theory M New York Springer - Verlag Gupta L Beomsu Chung Srinath M D et al Multichannel fusion models for the parametric classification of differential brain activity J IEEE Trans Biomed Eng M Lin C J Optimization support vector machines and machine learning Talk in DIS University of Rome and IASI CNR Italy EB /OL http / /www csie ntu edu tw / ~ cjlin /talks /rome pdf 10 Lin C J Support vector machines Tutorial talk at Machine Learning Summer School Taipei EB /OL 2006 http / / www csie ntu edu tw / ~ cjlin / talks / MLSS pdf 11 M Baddeley A Working memory J Science J Hyunseok Kook Lalit Guptab Srinivas Kotab et al An offline /real - time artifact rejection strategy to improve the classification of multi - channel evoked potentials J Pattern Recognition Gupta L Phegley J Molfese D L Parametric classification of multichannel evoked potentials J IEEE Trans Biomed Eng