EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 18; no. 5; pp. 1645 - 1666
Main Authors: Gu, Xiaotong, Cao, Zehong, Jolfaei, Alireza, Xu, Peng, Wu, Dongrui, Jung, Tzyy-Ping, Lin, Chin-Teng
Format: Journal Article
Language:English
Published: United States IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial intelligence
Brain
Brain research
Brain-Computer Interfaces
Cognitive ability
Computational neuroscience
Deep learning
EEG
Electroencephalography
Entertainment industry
Epilepsy - diagnosis
Epilepsy - physiopathology
Functional magnetic resonance imaging
Health care
Human-computer interface
Humans
Intelligence
Interfaces
Learning algorithms
Machine Learning
Monitoring
Polls & surveys
Sensors
Signal Processing, Computer-Assisted
Transfer learning
ISSN:1545-5963, 1557-9964, 1557-9964
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2021.3052811