CASIA OpenIR  > 多模态人工智能系统全国重点实验室
A learnable EEG channel selection method for MI-BCI using efficient channel attention
Tong, Lina1; Qian, Yihui1; Peng, Liang2; Wang, Chen2; Hou, Zeng-Guang2,3
Source PublicationFRONTIERS IN NEUROSCIENCE
2023-10-20
Volume17Pages:13
Corresponding AuthorPeng, Liang(liang.peng@ia.ac.cn) ; Wang, Chen(wangchen2016@ia.ac.cn)
AbstractIntroductionDuring electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy.MethodsThis paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a.Results and discussionThe proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.
Keywordbrain-computer interface motor imagery channel selection deep learning attention mechanism
DOI10.3389/fnins.2023.1276067
WOS KeywordMOTOR IMAGERY
Indexed BySCI
Language英语
Funding ProjectThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFC3601200, in part by the[2022YFC3601200] ; National Key Research and Development Program of China[62203441] ; National Key Research and Development Program of China[U21A20479] ; National Natural Science Foundation of China[4232053] ; National Natural Science Foundation of China[L222013] ; Beijing Natural Science Foundation
Funding OrganizationThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFC3601200, in part by the ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeurosciences
WOS IDWOS:001092205100001
PublisherFRONTIERS MEDIA SA
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/54299
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorPeng, Liang; Wang, Chen
Affiliation1.China Univ Min & Technol Beijing, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Tong, Lina,Qian, Yihui,Peng, Liang,et al. A learnable EEG channel selection method for MI-BCI using efficient channel attention[J]. FRONTIERS IN NEUROSCIENCE,2023,17:13.
APA Tong, Lina,Qian, Yihui,Peng, Liang,Wang, Chen,&Hou, Zeng-Guang.(2023).A learnable EEG channel selection method for MI-BCI using efficient channel attention.FRONTIERS IN NEUROSCIENCE,17,13.
MLA Tong, Lina,et al."A learnable EEG channel selection method for MI-BCI using efficient channel attention".FRONTIERS IN NEUROSCIENCE 17(2023):13.
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