Brain Computer Interface の出力情報量向上を目的とした複数の運動想起およびP300 の同時識別

カテゴリ: 研究会(論文単位)

論文No: MBE11121

グループ名: 【C】電子・情報・システム部門 医用・生体工学研究会

発行日: 2011/09/20

タイトル(英語): Simultaneous classification of multiple motor imagery and P300 for increase in output information of brain-computer interface

著者名: 内藤 玄造(東京大学),吉田 塁(東京大学),小川 雄太郎(東京大学),沼田 崇志(東京大学),小谷 潔(東京大学),神保 泰彦(東京大学)

著者名(英語): Naito Genzo(The University of Tokyo),Yoshida Lui(The University of Tokyo),Ogawa Yutaro(The University of Tokyo),Numata Takashi(The University of Tokyo),Kotani Kiyoshi(The University of Tokyo),Jimbo Yasuhiko(The University of Tokyo)

要約(日本語): Brain-Computer Interface (BCI) is a system to obtain information from the brain signal to control computers. P300 and motor imagery task of Electroencephalogram (EEG) are mainly used features for BCI. However, BCI with P300 classifies only two states and features of motor imagery task are too obscure to be classified easily. Therefore, we propose a method to increase the number of classified states with high accuracy by mixed signal processing for P300 and motor imaginary task. We design a experiment which gives 4 classes data-control, P300, and P300 during motor imagery of right or left hand. We classify them by multi-class Support Vector Machines, and show the efficacy of mixed signal which contain P300 and motor imagery.

原稿種別: 日本語

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