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：1113
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
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Important Dates
| Abstract submission date: |
2021-10-25 |
| Full paper submission date: |
2021-10-25 |
| Notification of acceptance date: |
2021-11-01 |
| Conference date: | 2021-11-12~2021-11-14 |
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