Publication detail

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

ARIF, M. REHMAN, F. SEKANINA, L. MALIK, A.

Original Title

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

Type

journal article - other

Language

English

Original 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.

Keywords

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

Authors

ARIF, M.; REHMAN, F.; SEKANINA, L.; MALIK, A.

Released

25. 9. 2024

ISBN

1741-2552

Periodical

Journal of Neural Engineering

Year of study

21

Number

5

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

25

Pages count

25

URL

https://iopscience.iop.org/article/10.1088/1741-2552/ad7f8e

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="1741-2552",
  url="https://iopscience.iop.org/article/10.1088/1741-2552/ad7f8e"
}