Abstract Details
68 / 2021-10-18 19:19:31
Classification of four-class motion imagination tasks based on EEG by combining EEG source imaging with convolution neural networks
EEG, EEG source imaging, convolution neural networks (CNNs)
Final Paper
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.
Important Date
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Conference Date
Nov 13
2021
toNov 14
2021
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Sep 30 2021
Contribution Submission Deadline
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Nov 14 2021
Registration deadline
Sponsored By
Medical Physics Branch of Chinese Society of Biomedical Engineering
IEEE Beijing Section
Life Electronics Society of Chinese Institute of Electronics
IEEE Beijing Section
Life Electronics Society of Chinese Institute of Electronics
Organized By
Anhui Biomedical Engineering Society.
University of Science and Technology of China
University of Science and Technology of China
Contact Information
- Mrs. Yang
- ic******@ustc.edu.cn
- +86********
Previous Conferences
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2017-11-04 China Beijing,China
2017 International Conference on Medical Imaging Physics and Engineering -
2017-11-03 China 海淀区
2017 IEEE 国际医学影像物理和工程大会 -
2013-10-19 China 沈阳市
2013 IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE)
