TY - JOUR
T1 - Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface
T2 - A Comprehensive Review
AU - Rashid, Mamunur
AU - Sulaiman, Norizam
AU - P. P. Abdul Majeed, Anwar
AU - Musa, Rabiu Muazu
AU - Ahmad, Ahmad Fakhri
AU - Bari, Bifta Sama
AU - Khatun, Sabira
N1 - Funding Information:
The authors would like to acknowledge support from the Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Malaysia. Funding. This work was supported by the Universiti Malaysia Pahang, Malaysia, through research grant FRGS/1/2018/TK04/UMP/02/3 (RDU190109).
Publisher Copyright:
© Copyright © 2020 Rashid, Sulaiman, P. P. Abdul Majeed, Musa, Ab. Nasir, Bari and Khatun.
PY - 2020/6/3
Y1 - 2020/6/3
N2 - Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.
AB - Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.
KW - brain-computer interface (BCI)
KW - classification
KW - electroencephalogram (EEG)
KW - feature extraction
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85086589297&partnerID=8YFLogxK
U2 - 10.3389/fnbot.2020.00025
DO - 10.3389/fnbot.2020.00025
M3 - Review article
AN - SCOPUS:85086589297
SN - 1662-5218
VL - 14
JO - Frontiers in Neurorobotics
JF - Frontiers in Neurorobotics
M1 - 25
ER -