[口头报告]Classification of four-class motion imagination tasks based on EEG by combining EEG source imaging with convolution neural networks - 报告详情

Classification of four-class motion imagination tasks based on EEG by combining EEG source imaging with convolution neural networks
编号:128 访问权限:仅限参会人 更新:2021-11-03 07:19:26 浏览:203次 口头报告

报告开始:2021年11月13日 16:25 (Asia/Shanghai)

报告时间:15min

所在会议:[PS1] Plenary Session 1 [NM1] Workshop on NM Session 1

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摘要
Abstract—Goal: With the rapid development of electroencephalogram (EEG) technology, the brain-computer interface (BCI) based on motion imagination (MI) has been widely used. Aiming at the problem of low classification accuracy of multi-task MI, this paper adopted an innovative method. Method: This paper combines EEG source imaging with convolution neural networks to optimize the classification problem. Result: The results showed that the proposed method improved the classification accuracy compared with other studies. Significance: Scouts and convolution neural networks are applied to classify EEG signals, which provides a new idea for classifying EEG signals.
关键字
EEG, EEG source imaging, convolution neural networks (CNNs)
报告人
璐 周
南京航空航天大学

稿件作者
璐 周 南京航空航天大学
桥桥 祝 南京航空航天大学
彪 伍 南京航空航天大学
兵 覃 南京航空航天大学
志余 钱 南京航空航天大学
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重要日期

摘要提交日期:

2021/08/31

2021/10/25

全文投稿日期:  

2021/09/15

2021/10/25

录取通知日期: 

2021/09/30

2021/11/01

会议日期:   2021-11-12-2021-11-14

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