EEG Feature Extraction Using Evolutionary Algorithms for Brain-Computer Interface Development.
Journal
Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357
Informations de publication
Date de publication:
2022
2022
Historique:
received:
22
02
2022
revised:
26
05
2022
accepted:
15
06
2022
entrez:
11
7
2022
pubmed:
12
7
2022
medline:
14
7
2022
Statut:
epublish
Résumé
Brain-computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain-computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem. An artificial neural network is used as a classifier for event detection, along with an evolutionary algorithm to find the optimal subset of electrodes and data points that better represents the target event. The obtained results indicate our approach is a competitive and viable alternative for feature extraction in electroencephalograms, leading to high accuracy values and allowing the reduction of a significant amount of data.
Identifiants
pubmed: 35814562
doi: 10.1155/2022/7571208
pmc: PMC9259297
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7571208Informations de copyright
Copyright © 2022 César Alfredo Rocha-Herrera et al.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest.
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