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

doi:10.1088/1741-2552/acf345

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems
	Wang, Kangning 2,5; Qiu, Shuang 3,5; Wei, Wei 5; Yi, Weibo 6; He, Huiguang 3,5; Xu, Minpeng 1,2; Jung, Tzyy-Ping 1,2,4; Ming, Dong 1,2
刊名	JOURNAL OF NEURAL ENGINEERING
	2023-10-01
卷号	20 期号:5 页码:15
关键词	brain-computer interface (BCI) electroencephalogram (EEG) vigilance estimation
ISSN号	1741-2560
DOI	10.1088/1741-2552/acf345
通讯作者	Qiu, Shuang(shuang.qiu@ia.ac.cn) ; Ming, Dong(richardming@tju.edu.cn)
英文摘要	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.
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]
WOS关键词	CONVOLUTIONAL NEURAL-NETWORK ; DIFFERENTIAL ENTROPY FEATURE ; BRAIN-COMPUTER INTERFACE ; RECOGNITION ; ATTENTION ; DELTA ; ALERTNESS ; STATES
WOS研究方向	Engineering ; Neurosciences & Neurology
语种	英语
出版者	IOP Publishing Ltd
WOS记录号	WOS:001059517100001
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54152]
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, Coll Precis Instruments & Optoelect Engn, Tianjin, Peoples R China 2.Tianjin Univ, Acad Med Engn & Translat Med, Tianjin, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Univ Calif San Diego, Swartz Ctr Computat Neurosci, La Jolla, CA USA 5.Chinese Acad Sci, Inst Automat, Lab Brain Atlas & Brain Inspired Intelligence, State Key Lab Multimodal Artificial Intelligence S, Beijing, Peoples R China 6.Beijing Machine & Equipment Inst, Beijing, Peoples R China
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.