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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F24%3APU152211" target="_blank" >RIV/00216305:26230/24:PU152211 - isvavai.cz</a>

Result on the web

<a href="https://iopscience.iop.org/article/10.1088/1741-2552/ad7f8e" target="_blank" >https://iopscience.iop.org/article/10.1088/1741-2552/ad7f8e</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1741-2552/ad7f8e" target="_blank" >10.1088/1741-2552/ad7f8e</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA24-10990S" target="_blank" >GA24-10990S: Hardware-Aware Machine Learning: From Automated Design to Innovative and Explainable Solutions</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Neural Engineering

ISSN

1741-2560

e-ISSN

1741-2552

Volume of the periodical

21

Issue of the periodical within the volume

5

Country of publishing house

GB - UNITED KINGDOM

Number of pages

25

Pages from-to

1-25

UT code for WoS article

001330142400001

EID of the result in the Scopus database

2-s2.0-85207348315