TY - GEN
T1 - Spatial Component-wise Convolutional Network (SCCNet) for Motor-Imagery EEG Classification
AU - Wei, Chun-Shu
AU - Koike-Akino, Toshiaki
AU - Wang, Ye
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/16
Y1 - 2019/5/16
N2 - We study brain-computer interfaces (BCI) based on the decoding of motor imagery (MI) from electroencephalography (EEG) neuromonitoring. The robustness of MI-BCI is a major concern in practical applications, and hence various efforts in the literature have been made to enhance the MI classification accuracy from EEG signals. Recently, classifiers based on convolutional neural networks (CNN) have achieved state-of-the-art performance. In further exploration of applying CNNs to EEG data, we propose a spatial component-wise convolutional network (SCCNet), featuring an initial convolutional layer for spatial filtering, a common processing in EEG analysis for signal enhancement and noise reduction. Through a series of optimization and validation, we show the superiority of SCCNet in MI EEG classification, outperforming other existing CNNs.
AB - We study brain-computer interfaces (BCI) based on the decoding of motor imagery (MI) from electroencephalography (EEG) neuromonitoring. The robustness of MI-BCI is a major concern in practical applications, and hence various efforts in the literature have been made to enhance the MI classification accuracy from EEG signals. Recently, classifiers based on convolutional neural networks (CNN) have achieved state-of-the-art performance. In further exploration of applying CNNs to EEG data, we propose a spatial component-wise convolutional network (SCCNet), featuring an initial convolutional layer for spatial filtering, a common processing in EEG analysis for signal enhancement and noise reduction. Through a series of optimization and validation, we show the superiority of SCCNet in MI EEG classification, outperforming other existing CNNs.
UR - http://www.scopus.com/inward/record.url?scp=85066765529&partnerID=8YFLogxK
U2 - 10.1109/NER.2019.8716937
DO - 10.1109/NER.2019.8716937
M3 - Conference contribution
AN - SCOPUS:85066765529
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 328
EP - 331
BT - 9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PB - IEEE Computer Society
T2 - 9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Y2 - 20 March 2019 through 23 March 2019
ER -