39 1 2011 2 Journal of Fuzhou University Natural Science Edition Vol 39 No 1 Feb 2011 DOI CNKI 35-1117 /N 20110121 1723 008 1000-2243 2011 01-0054 - 06 ERPs 350108 - 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 350108 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 - 4 5 Kolmogrov - Smirnov test K - S test ERPs 2010-06 - 17 1971 - E - mail hzh@ fzu edu cn 2009 - XQ - 25 2009H0013
1 ERPs 55 support vector machines SVM 6 SVM ERPs ERPs 3% ~ 7% SVM 90% 1 1 1 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 64 1 000 Hz 500 ms 64 500 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 9 10 2 SVM SVM VAPNIK 6 11 Mercer SVM 1
56 39 2 / 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 6 1 2 wt w + C n min ξ i w b ξ i = 1 s t y i w T Φ x i + b - 1 + ξ i 0 ξ i 0 w ξ Φ x i R h y i - 1 1 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 = 1 1 1 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 3 3 1 12 13 4 1 8 + + 4 Fig 1 1 Events and durations composing single - trial of data acquisition
1 ERPs 57 D1 D2 R1 R2 4 6 80 10-20 64 2 Neuro Scan 1 000 Hz 3 2 Neuro Scan 1 2 3 200 ms 499 ms 700 ms 4 200 ms 5 6 500 ms 7 C m n 3 3 4 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 350 700 700 400 300 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 1-1 1 2 100% 3 4 2 0 499 1 500 1 64 A D2 R2 C m n K - S test Z m n 9
58 39 Z i j 1 2 i - 1 j 2 2 ERPs K - S test 1 Z m n 512 3% ~ 7% 2 6 6 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 64 500 Dimension of features extracted based on K - S test original data space is 64 500 A B C D E F D1 D2 1 881 1 765 2 022 1 686 2 143 2 278 D1 R1 2 012 1 896 1 986 1 723 1 952 2 107 D1 R2 1 756 1 952 1 893 1 605 2 017 1 975 D2 R1 1 503 1 608 1 809 1 542 1 896 1 739 D2 R2 1 208 1 523 1 653 1 387 1 498 1 503 R1 R2 1 063 1 148 1 005 1 178 1 189 1 098 2 Tab 2 Classification accuracy of two different brainwave states % P A P B P C P D P E P F D1 D2 94 67 95 67 99 33 95 00 99 67 98 33 D1 R1 95 33 96 00 95 33 92 67 97 33 94 00 D1 R2 94 33 94 33 96 67 97 33 94 33 96 67 D2 R1 93 67 93 33 95 00 94 67 96 33 95 67 D2 R2 98 33 95 33 97 67 99 67 95 00 94 33 R1 R2 97 00 96 67 98 33 94 33 95 67 99 33
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