A learnable EEG channel selection method for MI-BCI using efficient channel attention

Frontiers in Neuroscience

Neuroscience

Yihui Qian¹  Lina Tong¹  Liang Peng²  Chen Wang²  Zeng-Guang Hou³ 

[1] China University of Mining and Technology-Beijing, Beijing, China;State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China;State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences (CAS) Center for Excellence in Brain Science and Intelligence Technology, Beijing, China;

关键词: brain-computer interface;    motor imagery;    channel selection;    deep learning;    attention mechanism;   

DOI  :  10.3389/fnins.2023.1276067

 received in 2023-08-11, accepted in 2023-10-05,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionDuring 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.

【 授权许可】

Unknown   
Copyright © 2023 Tong, Qian, Peng, Wang and Hou.