Deep recurrent spatiooral neural network for motor imagery based BCI

Wonjun Ko, Jeeseok Yoon, Eunsong Kang, Eunji Jun, Jun Sik Choi, Heung Il Suk

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    38 Citations (Scopus)

    Abstract

    In this paper, we propose a novel architecture of a deep neural network for EEG-based motor imagery classification. Unlike the existing deep neural networks in the literature, the proposed network allows us to analyze the learned network weights from a neurophysiological perspective, thus providing an insight into the underlying patterns inherent in motor imagery induced EEG signals. In order to validate the effectiveness of the proposed method, we conducted experiments on the BCI Competition IV-IIa dataset by comparing with the competing methods in terms of the Cohen's k value. For qualitative analysis, we also performed visual inspection of the activation patterns estimated from the learned network weights.

    Original languageEnglish
    Title of host publication2018 6th International Conference on Brain-Computer Interface, BCI 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1-3
    Number of pages3
    ISBN (Electronic)9781538625743
    DOIs
    Publication statusPublished - 2018 Mar 9
    Event6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of
    Duration: 2018 Jan 152018 Jan 17

    Publication series

    Name2018 6th International Conference on Brain-Computer Interface, BCI 2018
    Volume2018-January

    Other

    Other6th International Conference on Brain-Computer Interface, BCI 2018
    Country/TerritoryKorea, Republic of
    CityGangWon
    Period18/1/1518/1/17

    Bibliographical note

    Publisher Copyright:
    © 2018 IEEE.

    Keywords

    • Brain-Computer Interface
    • Deep Learning
    • Electroencephalogram
    • Motor Imagery
    • Recurrent Convolutional Neural Network

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Human-Computer Interaction
    • Behavioral Neuroscience

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