TY - JOUR
T1 - EEG-based Brain-Computer Interfaces (BCIs)
T2 - a survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications
AU - Gu, Xiaotong
AU - Cao, Zehong
AU - Jolfaei, Alireza
AU - Xu, Peng
AU - Wu, Dongrui
AU - Jung, Tzyy-Ping
AU - Lin, Chin-Teng
PY - 2021/9
Y1 - 2021/9
N2 - Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.
AB - Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.
KW - Brain
KW - Deep learning
KW - Electroencephalography
KW - BCI
KW - IoT
KW - Encryption
KW - Sensors
KW - Security
KW - Internet of Medical Things
UR - http://www.scopus.com/inward/record.url?scp=85099726197&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DP180100670
UR - http://purl.org/au-research/grants/arc/DP180100656
U2 - 10.1109/TCBB.2021.3052811
DO - 10.1109/TCBB.2021.3052811
M3 - Article
C2 - 33465029
AN - SCOPUS:85099726197
SN - 1545-5963
VL - 18
SP - 1645
EP - 1666
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 5
ER -