한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제23권2호
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  • Pages.178-183
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  • 2013
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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Binary Classification Method using Invariant CSP for Hand Movements Analysis in EEG-based BCI System

  • 응웬탄하 (중앙대학교 전자전기공학부) ;
  • 박승민 (중앙대학교 전자전기공학부) ;
  • 고광은 (중앙대학교 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Nguyen, Thanh Ha (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 : 2013.03.08
  • 심사 : 2013.03.29
  • 발행 : 2013.04.25
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초록

In this study, we proposed a method for electroencephalogram (EEG) classification using invariant CSP at special channels for improving the accuracy of classification. Based on the naive EEG signals from left and right hand movement experiment, the noises of contaminated data set should be eliminate and the proposed method can deal with the de-noising of data set. The considering data set are collected from the special channels for right and left hand movements around the motor cortex area. The proposed method is based on the fit of the adjusted parameter to decline the affect of invariant parts in raw signals and can increase the classification accuracy. We have run the simulation for hundreds time for each parameter and get averaged value to get the last result for comparison. The experimental results show the accuracy is improved more than the original method, the highest result reach to 89.74%.

키워드

참고문헌

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피인용 문헌

  1. EEG Feature Classification for Precise Motion Control of Artificial Hand vol.25, pp.1, 2015, https://doi.org/10.5391/JKIIS.2015.25.1.029