Classification of four-class motion imagination tasks based on EEG by combining EEG source imaging with convolution neural networks
编号：128
稿件编号：68 访问权限：仅限参会人
更新：2021-11-03 07:19:26
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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)
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