中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 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

文献类型：期刊论文

作者Wang, Kangning2,3; Qiu, Shuang1,3; Wei, Wei3; Yi, Weibo4; He, Huiguang1,3; Xu, Minpeng2,5; Jung, Tzyy-Ping2,5,6; Ming, Dong2,5
刊名JOURNAL OF NEURAL ENGINEERING
出版日期2023-10-01
卷号20期号:5页码:15
ISSN号1741-2560
关键词brain-computer interface (BCI) electroencephalogram (EEG) vigilance estimation
DOI10.1088/1741-2552/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.
WOS关键词CONVOLUTIONAL NEURAL-NETWORK ; DIFFERENTIAL ENTROPY FEATURE ; BRAIN-COMPUTER INTERFACE ; RECOGNITION ; ATTENTION ; DELTA ; ALERTNESS ; STATES
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
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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Tianjin Univ, Acad Med Engn & Translat Med, Tianjin, Peoples R China
3.Chinese Acad Sci, Inst Automat, Lab Brain Atlas & Brain Inspired Intelligence, State Key Lab Multimodal Artificial Intelligence S, 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
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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。