Efficient Brain Decoding Based on Adaptive EEG Channel Selection and Transformation

doi:10.1109/TETCI.2022.3147225

	Efficient Brain Decoding Based on Adaptive EEG Channel Selection and Transformation
	Wang, Jiaxing1 ; Shi, Lei2 ; Wang, Weiqun1 ; Hou, Zeng-Guang1,3,4,5
发表期刊	IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
ISSN	2471-285X
	2022-03-08
页码	10
通讯作者	Hou, Zeng-Guang(zeng-guang.hou@ia.ac.cn)
摘要	Electroencephalography (EEG) based brain-computer interface (BCI) has a wide range of applications in neuro-rehabilitation and motor assistance. However, brain activities, acquired from a large number of EEG channels, are highly inter-correlated or irrelevant to the brain decoding task, thus reducing the decoding efficiency and accuracy. How to adaptively select the optimal channel number depend on different trials remains a big challenge. To solve this problem, an efficient end-to-end brain decoding model named AdaEEGNet, is proposed in this study. It can reduce the computational cost by adaptively controlling the number of input channels and improve the classification accuracy by reducing over-fitting. Specifically, a lightweight policy module is designed to analyze which channel is needed for decoding current EEG trial. Due to the channel selection process is indifferentiable, we propose to use the Gumbel-Estimator to back-propagate the gradient to train the whole framework. Additionally, a weight coefficient is designed to make a trade-off between brain decoding accuracy and efficiency. To validate the proposed AdaEEGNet feasibility in improving decoding efficiency and accuracy, extensive experiments were conducted on BCI competition IV dataset. The results show that our methods can improve the decoding accuracy by 2% with only 65% computational cost significantly compared with the baseline method.
关键词	Electroencephalography Decoding Brain modeling Computational modeling Optimized production technology Feature extraction Data models Channel selection channel transformation brain decoding computational cost classification accuracy
DOI	10.1109/TETCI.2022.3147225
关键词[WOS]	COMMON SPATIAL-PATTERN ; MOTOR IMAGERY ; COMPUTER-INTERFACE ; CLASSIFICATION ; BCI ; NETWORK ; SYSTEM ; SSVEP
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61720106012] ; National Natural Science Foundation of China[U1913601] ; National Key R&D Program of China[2018YFC2001700] ; Beijing Natural Science Foundation[4202074] ; Beijing Science and Technology Project[Z211100007921021] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32000000]
项目资助者	National Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Natural Science Foundation ; Beijing Science and Technology Project ; Strategic Priority Research Program of Chinese Academy of Science
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000767853500001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	多模态智能
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47991
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Hou, Zeng-Guang
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Meituan Inc, Beijing 100102, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.CAS Ctr Excellence Brain Sci & Intelligence Tech, Beijing 100190, Peoples R China 5.Macau Univ Sci & Technol, Inst Syst Engn, MUST CASIA Joint Lab Intelligence Sci & Technol, Macau, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wang, Jiaxing,Shi, Lei,Wang, Weiqun,et al. Efficient Brain Decoding Based on Adaptive EEG Channel Selection and Transformation[J]. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,2022:10.
APA	Wang, Jiaxing,Shi, Lei,Wang, Weiqun,&Hou, Zeng-Guang.(2022).Efficient Brain Decoding Based on Adaptive EEG Channel Selection and Transformation.IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,10.
MLA	Wang, Jiaxing,et al."Efficient Brain Decoding Based on Adaptive EEG Channel Selection and Transformation".IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2022):10.