Exploring the temporal and spatial features of EEG signals in motor imagery task using Deep Learning

カテゴリ: 研究会(論文単位)

論文No: MBE18026

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: 2018/03/20

タイトル(英語): Exploring the temporal and spatial features of EEG signals in motor imagery task using Deep Learning

著者名: Gu Feng(Graduate School of Engineering The University of Tokyo),Kobayashi Yuya(Graduate School of Engineering The University of Tokyo),Shirasaka Sho(The University of Tokyo),Kotani Kiyoshi(Graduate School of Engineering University of Tokyo),Jimbo Yasuhiko(Graduate School of Engineering University of Tokyo)

著者名(英語): Feng Gu(Graduate School of Engineering The University of Tokyo),Yuya Kobayashi(Graduate School of Engineering The University of Tokyo),Sho Shirasaka(The University of Tokyo),Kiyoshi Kotani(Graduate School of Engineering University of Tokyo),Yasuhiko Jimbo(Graduate School of Engineering University of Tokyo)

キーワード: BCI|Deep Learning|Motor Imagery|EEG|CNN|RNN|BCI|Deep Learning|Motor Imagery|EEG|CNN|RNN

要約(日本語): Recently the deep learning has outperformed classical method in many tasks. Since extracting useful features is the key point in BCI research, it is reasonable to assume that deep neuron networks can extract useful features from raw data. We will focus on how to use CNN and RNN to extract spatial and temporal features and gain better result on open dataset.

要約(英語): Recently the deep learning has outperformed classical method in many tasks. Since extracting useful features is the key point in BCI research, it is reasonable to assume that deep neuron networks can extract useful features from raw data. We will focus on how to use CNN and RNN to extract spatial and temporal features and gain better result on open dataset.

原稿種別: 英語

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