Spatial Component-wise Convolutional Network (SCCNet) for Motor-Imagery EEG Classification

Chun-Shu Wei, Toshiaki Koike-Akino, Ye Wang

研究成果: Conference contribution同行評審

26 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題9th International IEEE EMBS Conference on Neural Engineering, NER 2019
發行者IEEE Computer Society
頁面328-331
頁數4
ISBN(電子)9781538679210
DOIs
出版狀態Published - 16 5月 2019
事件9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
持續時間: 20 3月 201923 3月 2019

出版系列

名字International IEEE/EMBS Conference on Neural Engineering, NER
2019-March
ISSN(列印)1948-3546
ISSN(電子)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
國家/地區United States
城市San Francisco
期間20/03/1923/03/19

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