한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제22권6호
  • /
  • Pages.793-798
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  • 2012
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface

  • Chum, Pharino (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Ko, Kwang-Eun (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 투고 : 2012.11.10
  • 심사 : 2012.12.03
  • 발행 : 2012.12.25
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초록

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.

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과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

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