Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems

doi:10.1088/1741-2552/acf345

	Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems
	Wang, Kangning 1,2; Qiu, Shuang2,3 ; Wei, Wei2 ; Yi, Weibo 4; He, Huiguang2,3 ; Xu, Minpeng 1,5; Jung, Tzyy-Ping 1,5,6; Ming, Dong 1,5
发表期刊	JOURNAL OF NEURAL ENGINEERING
ISSN	1741-2560
	2023-10-01
卷号	20 期号:5 页码:15
通讯作者	Qiu, Shuang(shuang.qiu@ia.ac.cn) ; Ming, Dong(richardming@tju.edu.cn)
摘要	Objective. The state of vigilance is crucial for effective performance in brain-computer interface (BCI) tasks, and therefore, it is essential to investigate vigilance levels in BCI tasks. Despite this, most studies have focused on vigilance levels in driving tasks rather than on BCI tasks, and the electroencephalogram (EEG) patterns of vigilance states in different BCI tasks remain unclear. This study aimed to identify similarities and differences in EEG patterns and performances of vigilance estimation in different BCI tasks and sessions. Approach. To achieve this, we built a steady-state visual evoked potential-based BCI system and a rapid serial visual presentation-based BCI system and recruited 18 participants to carry out four BCI experimental sessions over four days. Main results. Our findings demonstrate that specific neural patterns for high and low vigilance levels are relatively stable across sessions. Differential entropy features significantly differ between different vigilance levels in all frequency bands and between BCI tasks in the delta and theta frequency bands, with the theta frequency band features playing a critical role in vigilance estimation. Additionally, prefrontal, temporal, and occipital regions are more relevant to the vigilance state in BCI tasks. Our results suggest that cross-session vigilance estimation is more accurate than cross-task estimation. Significance. Our study clarifies the underlying mechanisms of vigilance state in two BCI tasks and provides a foundation for further research in vigilance estimation in BCI applications.
关键词	brain-computer interface (BCI) electroencephalogram (EEG) vigilance estimation
DOI	10.1088/1741-2552/acf345
关键词[WOS]	CONVOLUTIONAL NEURAL-NETWORK ; DIFFERENTIAL ENTROPY FEATURE ; BRAIN-COMPUTER INTERFACE ; RECOGNITION ; ATTENTION ; DELTA ; ALERTNESS ; STATES
收录类别	SCI
语种	英语
资助项目	This work was supported by the Beijing Natural Science Foundation (7222311 and J210010), the National Natural Science Foundation of China (U21A20388, 62276262, 62206285, 62006014).[J210010] ; This work was supported by the Beijing Natural Science Foundation (7222311 and J210010), the National Natural Science Foundation of China (U21A20388, 62276262, 62206285, 62006014).[U21A20388] ; Beijing Natural Science Foundation[62276262] ; Beijing Natural Science Foundation[62206285] ; Beijing Natural Science Foundation[62006014] ; National Natural Science Foundation of China ; [7222311]
项目资助者	This work was supported by the Beijing Natural Science Foundation (7222311 and J210010), the National Natural Science Foundation of China (U21A20388, 62276262, 62206285, 62006014). ; Beijing Natural Science Foundation ; National Natural Science Foundation of China
WOS研究方向	Engineering ; Neurosciences & Neurology
WOS类目	Engineering, Biomedical ; Neurosciences
WOS记录号	WOS:001059517100001
出版者	IOP Publishing Ltd
引用统计	被引频次：1[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/173211/54153
专题	多模态人工智能系统全国重点实验室
通讯作者	Qiu, Shuang; Ming, Dong
作者单位	1.Tianjin Univ, Acad Med Engn & Translat Med, Tianjin, Peoples R China 2.Chinese Acad Sci, Inst Automat, Lab Brain Atlas & Brain Inspired Intelligence, State Key Lab Multimodal Artificial Intelligence S, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Beijing Machine & Equipment Inst, Beijing, Peoples R China 5.Tianjin Univ, Coll Precis Instruments & Optoelect Engn, Tianjin, Peoples R China 6.Univ Calif San Diego, Swartz Ctr Computat Neurosci, La Jolla, CA USA
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, Kangning,Qiu, Shuang,Wei, Wei,et al. Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems[J]. JOURNAL OF NEURAL ENGINEERING,2023,20(5):15.
APA	Wang, Kangning.,Qiu, Shuang.,Wei, Wei.,Yi, Weibo.,He, Huiguang.,...&Ming, Dong.(2023).Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems.JOURNAL OF NEURAL ENGINEERING,20(5),15.
MLA	Wang, Kangning,et al."Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems".JOURNAL OF NEURAL ENGINEERING 20.5(2023):15.