TY - GEN
T1 - Deep recurrent spatiooral neural network for motor imagery based BCI
AU - Ko, Wonjun
AU - Yoon, Jeeseok
AU - Kang, Eunsong
AU - Jun, Eunji
AU - Choi, Jun Sik
AU - Suk, Heung Il
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451). All correspondence should be directed to Heung-Il Suk.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/3/9
Y1 - 2018/3/9
N2 - 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.
AB - 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.
KW - Brain-Computer Interface
KW - Deep Learning
KW - Electroencephalogram
KW - Motor Imagery
KW - Recurrent Convolutional Neural Network
UR - http://www.scopus.com/inward/record.url?scp=85050790413&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2018.8311535
DO - 10.1109/IWW-BCI.2018.8311535
M3 - Conference contribution
AN - SCOPUS:85050790413
T3 - 2018 6th International Conference on Brain-Computer Interface, BCI 2018
SP - 1
EP - 3
BT - 2018 6th International Conference on Brain-Computer Interface, BCI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Brain-Computer Interface, BCI 2018
Y2 - 15 January 2018 through 17 January 2018
ER -