[Oral Presentation]Classification of four-class motion imagination tasks based on EEG by combining EEG source imaging with convolution neural networks - Contribution details

Classification of four-class motion imagination tasks based on EEG by combining EEG source imaging with convolution neural networks
ID:128 Submission ID:68 View Protection:ATTENDEE Updated Time:2021-11-03 07:19:26 Hits:242 Oral Presentation

Start Time:2021-11-13 16:25 (Asia/Shanghai)

Duration:15min

Session:[PS1] Plenary Session 1 [NM1] Workshop on NM Session 1

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Abstract
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.
Keywords
EEG, EEG source imaging, convolution neural networks (CNNs)
Speaker
璐 周
南京航空航天大学

Submission Author
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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Important Dates

Abstract submission date:

2021-08-31

2021-10-25

Full paper submission date:

2021-09-15

2021-10-25

Notification of acceptance date: 

2021-09-30

2021-11-01

Conference date: 2021-11-12~2021-11-14

 

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