Affective Brain-Computer Interfaces (aBCIs): A Tutorial

被引:39
作者
Wu, Dongrui [1 ]
Lu, Bao-Liang [2 ]
Hu, Bin [3 ]
Zeng, Zhigang [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Key Lab, Minist Educ Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
[2] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China
[3] Beijing Inst Technol, Inst Engn Med, Beijing 100811, Peoples R China
关键词
Affective computing; brain-computer interface (BCI); emotion recognition; emotion regulation; machine learning; MULTIMODAL EMOTION RECOGNITION; DIFFERENTIAL ENTROPY FEATURE; EEG ALPHA-ACTIVITY; TIME-SERIES; COMPONENT ANALYSIS; CLASSIFICATION; CONNECTIVITY; SPEECH; RESPONSES; DEFICITS;
D O I
10.1109/JPROC.2023.3277471
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A brain-computer interface (BCI) enables a user to communicate directly with a computer using only the central nervous system. An affective BCI (aBCI) monitors and/or regulates the emotional state of the brain, which could facilitate human cognition, communication, decision-making, and health. The last decade has witnessed rapid progress in aBCI research and applications, but there does not exist a comprehensive and up-to-date tutorial on aBCIs. This tutorial fills the gap. It introduces first the basic concepts of BCIs and then, in detail, the individual components in a closed-loop aBCI system, including signal acquisition, signal processing, feature extraction, emotion recognition, and brain stimulation. Next, it describes three representative applications of aBCIs, i.e., cognitive workload recognition, fatigue estimation, and depression diagnosis and treatment. Several challenges and opportunities in aBCI research and applications, including brain signal acquisition, emotion labeling, diversity and size of aBCI datasets, algorithm comparison, negative transfer in emotion recognition, and privacy protection and security of aBCIs, are also explained.
引用
收藏
页码:1314 / 1332
页数:19
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