Publication Details
A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications
REHMAN, F.
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Malik Aamir Saeed, prof., Ph.D. (DCSY)
Evolutionary algorithms, Electroencephalography, EEG, optimization,
nature-inspired metaheuristics
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.
@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"
}