Publication Details

A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications

ARIF Muhammad, REHMAN (ur) Faizan, SEKANINA Lukáš and MALIK Aamir Saeed. A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications. Journal of Neural Engineering, vol. 21, no. 5, 2024, pp. 1-25. ISSN 1741-2552. Available from: https://iopscience.iop.org/article/10.1088/1741-2552/ad7f8e
Czech title
Komplexní přehled evolučních algoritmů a metaheuristiky v aplikacích založených na EEG mozku
Type
journal article
Language
english
Authors
Arif Muhammad, Ph.D. (DCSY FIT BUT)
Rehman (ur) Faizan ()
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY FIT BUT)
Malik Aamir Saeed, Ph.D. (DCSY FIT BUT)
URL
Keywords

Evolutionary algorithms, Electroencephalography, EEG, optimization, nature-inspired metaheuristics

Abstract

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces (BCI). Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.

Published
2024
Pages
1-25
Journal
Journal of Neural Engineering, vol. 21, no. 5, ISSN 1741-2552
Publisher
IOP Publishing
DOI
BibTeX
@ARTICLE{FITPUB13269,
   author = "Muhammad Arif and Faizan (ur) Rehman and Luk\'{a}\v{s} Sekanina and Saeed Aamir Malik",
   title = "A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications",
   pages = "1--25",
   journal = "Journal of Neural Engineering",
   volume = 21,
   number = 5,
   year = 2024,
   ISSN = "1741-2552",
   doi = "10.1088/1741-2552/ad7f8e",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/13269"
}
Back to top