Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Improved Feature Extraction of Hand Movement EEG Signals based on Independent Component Analysis and Spatial Filter

  • 응웬탄하 (중앙대학교 전자전기공학부) ;
  • 박승민 (중앙대학교 전자전기공학부) ;
  • 고광은 (중앙대학교 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Received : 2012.04.20
  • Accepted : 2012.07.21
  • Published : 2012.08.25
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Abstract

In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).

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References

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