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Accuracy Comparison of Motor Imagery Performance Evaluation Factors Using EEG Based Brain Computer Interface by Neurofeedback Effectiveness

뉴로피드백 효과에 따른 EEG 기반 BCI 동작 상상 성능 평가 요소별 정확도 비교

  • Choi, Dong-Hag (Department of Electrical & Electronic Engineering, Yonsei University) ;
  • Ryu, Yon-Su (Department of Electrical & Electronic Engineering, Yonsei University) ;
  • Lee, Young-Bum (Department of Electrical & Electronic Engineering, Yonsei University) ;
  • Min, Se-Dong (Department of Electrical & Electronic Engineering, Yonsei University) ;
  • Lee, Myoung-Ho (Department of Electrical & Electronic Engineering, Yonsei University)
  • 최동학 (연세대학교 전기전자공학과) ;
  • 류연수 (연세대학교 전기전자공학과) ;
  • 이영범 (연세대학교 전기전자공학과) ;
  • 민세동 (연세대학교 전기전자공학과) ;
  • 이명호 (연세대학교 전기전자공학과)
  • Received : 2011.04.30
  • Accepted : 2011.07.20
  • Published : 2011.12.30

Abstract

In this study, we evaluated the EEG based BCI algorithm using common spatial pattern to find realistic applicability using neurofeedback EEG based BCI algorithm - EEG mode, feature vector calculation, the number of selected channels, 3 types of classifier, window size is evaluated for 10 subjects. The experimental results have been evaluated depending on conditioned experiment whether neurofeedback is used or not In case of using neurofeedback, a few subjects presented exceptional but general tendency presented the performance improvement Through this study, we found a motivation of development for the specific classifier based BCI system and the assessment evaluation system. We proposed a need for an optimized algorithm applicable to the robust motor imagery evaluation system with more useful functionalities.

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