TY - JOUR
T1 - Affective Brain-Computer Interfaces (aBCIs)
T2 - A Tutorial
AU - Wu, Dongrui
AU - Lu, Bao Liang
AU - Hu, Bin
AU - Zeng, Zhigang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - 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.
AB - 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.
KW - Affective computing
KW - brain-computer interface (BCI)
KW - emotion recognition
KW - emotion regulation
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85161035854&partnerID=8YFLogxK
U2 - 10.1109/JPROC.2023.3277471
DO - 10.1109/JPROC.2023.3277471
M3 - Article
AN - SCOPUS:85161035854
SN - 0018-9219
VL - 111
SP - 1314
EP - 1332
JO - Proceedings of the IEEE
JF - Proceedings of the IEEE
IS - 10
ER -
