Detail výsledku

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

ARIF, M.; REHMAN, F.; SEKANINA, L.; MALIK, A. A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications. Journal of Neural Engineering, 2024, vol. 21, no. 5, p. 1-25. ISSN: 1741-2552.
Typ
článek v časopise
Jazyk
anglicky
Autoři
Arif Muhammad, Ph.D.
REHMAN, F.
Sekanina Lukáš, prof. Ing., Ph.D., UPSY (FIT)
Malik Aamir Saeed, prof., Ph.D., UPSY (FIT)
Abstrakt

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.

Klíčová slova

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

URL
Rok
2024
Strany
1–25
Časopis
Journal of Neural Engineering, roč. 21, č. 5, ISSN 1741-2552
DOI
UT WoS
001330142400001
EID Scopus
BibTeX
@article{BUT189698,
  author="ARIF, M. and REHMAN, F. and SEKANINA, L. and MALIK, A.",
  title="A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications",
  journal="Journal of Neural Engineering",
  year="2024",
  volume="21",
  number="5",
  pages="1--25",
  doi="10.1088/1741-2552/ad7f8e",
  issn="1741-2560",
  url="https://iopscience.iop.org/article/10.1088/1741-2552/ad7f8e"
}
Projekty
Strojové učení zohledňující hardware: Od automatizovaného návrhu k inovativním a vysvětlitelným řešením, GAČR, Standardní projekty, GA24-10990S, zahájení: 2024-01-01, ukončení: 2026-12-31, řešení
Výzkumné skupiny
Pracoviště
Nahoru