Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022)
Mental fatigue is a psychophysical condition with a significant adverse effect on daily life, compromising both physical and mental wellness. We are experiencing challenges in this fast-changing environment, and mental fatigue problems are becoming more prominent. This demands an urgent need to expl...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10185973/ |
| _version_ | 1827892499731972096 |
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| author | Hamwira Yaacob Farhad Hossain Sharunizam Shari Smith K. Khare Chui Ping Ooi U. Rajendra Acharya |
| author_facet | Hamwira Yaacob Farhad Hossain Sharunizam Shari Smith K. Khare Chui Ping Ooi U. Rajendra Acharya |
| author_sort | Hamwira Yaacob |
| collection | DOAJ |
| description | Mental fatigue is a psychophysical condition with a significant adverse effect on daily life, compromising both physical and mental wellness. We are experiencing challenges in this fast-changing environment, and mental fatigue problems are becoming more prominent. This demands an urgent need to explore an effective and accurate automated system for timely mental fatigue detection. Therefore, we present a systematic review of brain-computer interface (BCI) studies for mental fatigue detection using artificial intelligent (AI) techniques published in Scopus, IEEE Explore, PubMed and Web of Science (WOS) between 2011 and 2022. The Boolean search expression that comprised (((ELECTROENCEPHALOGRAM) AND (BCI)) AND (FATIGUE CLASSIFICATION)) AND (BRAIN-COMPUTER INTERFACE) has been used to select the articles. Through the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, we selected 39 out of 562 articles. Our review identified the research gap in employing BCI for mental fatigue intervention through automated neurofeedback. The AI techniques employed to develop EEG-based mental fatigue detection are also discussed. We have presented comprehensive challenges and future recommendations from the gaps identified in discussions. The future direction includes data fusion, hybrid classification models, availability of public datasets, uncertainty, explainability, and hardware implementation strategies. |
| first_indexed | 2024-03-12T21:40:37Z |
| format | Article |
| id | doaj.art-9ee4c91f40254b3ca7ba82e5fc0c4ac0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T21:40:37Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9ee4c91f40254b3ca7ba82e5fc0c4ac02023-07-26T23:00:44ZengIEEEIEEE Access2169-35362023-01-0111747367475810.1109/ACCESS.2023.329638210185973Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022)Hamwira Yaacob0https://orcid.org/0000-0003-2346-6391Farhad Hossain1https://orcid.org/0000-0001-9216-4552Sharunizam Shari2https://orcid.org/0000-0003-0052-8174Smith K. Khare3https://orcid.org/0000-0001-8365-1092Chui Ping Ooi4U. Rajendra Acharya5Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, MalaysiaKulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, MalaysiaCollege of Computing, Informatics and Media, Universiti Teknologi MARA Cawangan Kedah, Merbok, MalaysiaElectrical and Computer Engineering Department, Aarhus University, Aarhus, DenmarkSchool of Science and Technology, Singapore University of Social Sciences, Clementi Road, SingaporeSchool of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central, QLD, AustraliaMental fatigue is a psychophysical condition with a significant adverse effect on daily life, compromising both physical and mental wellness. We are experiencing challenges in this fast-changing environment, and mental fatigue problems are becoming more prominent. This demands an urgent need to explore an effective and accurate automated system for timely mental fatigue detection. Therefore, we present a systematic review of brain-computer interface (BCI) studies for mental fatigue detection using artificial intelligent (AI) techniques published in Scopus, IEEE Explore, PubMed and Web of Science (WOS) between 2011 and 2022. The Boolean search expression that comprised (((ELECTROENCEPHALOGRAM) AND (BCI)) AND (FATIGUE CLASSIFICATION)) AND (BRAIN-COMPUTER INTERFACE) has been used to select the articles. Through the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, we selected 39 out of 562 articles. Our review identified the research gap in employing BCI for mental fatigue intervention through automated neurofeedback. The AI techniques employed to develop EEG-based mental fatigue detection are also discussed. We have presented comprehensive challenges and future recommendations from the gaps identified in discussions. The future direction includes data fusion, hybrid classification models, availability of public datasets, uncertainty, explainability, and hardware implementation strategies.https://ieeexplore.ieee.org/document/10185973/Brain-computer interface (BCI)electroencephalogram (EEG)mental fatigue detectionPRISMA |
| spellingShingle | Hamwira Yaacob Farhad Hossain Sharunizam Shari Smith K. Khare Chui Ping Ooi U. Rajendra Acharya Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022) IEEE Access Brain-computer interface (BCI) electroencephalogram (EEG) mental fatigue detection PRISMA |
| title | Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022) |
| title_full | Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022) |
| title_fullStr | Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022) |
| title_full_unstemmed | Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022) |
| title_short | Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022) |
| title_sort | application of artificial intelligence techniques for brain x2013 computer interface in mental fatigue detection a systematic review 2011 x2013 2022 |
| topic | Brain-computer interface (BCI) electroencephalogram (EEG) mental fatigue detection PRISMA |
| url | https://ieeexplore.ieee.org/document/10185973/ |
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