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
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

7571208

Informations 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.

Références

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Auteurs

César Alfredo Rocha-Herrera (CA)

Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.

Alan Díaz-Manríquez (A)

Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.

Jose Hugo Barron-Zambrano (JH)

Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.

Juan Carlos Elizondo-Leal (JC)

Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.

Vicente Paul Saldivar-Alonso (VP)

Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.

Jose Ramon Martínez-Angulo (JR)

Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.

Marco Aurelio Nuño-Maganda (MA)

Intelligent Systems Department, Polytechnic University of Victoria, Ciudad Victoria 87138, Tamaulipas, Mexico.

Said Polanco-Martagon (S)

Intelligent Systems Department, Polytechnic University of Victoria, Ciudad Victoria 87138, Tamaulipas, Mexico.

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Classifications MeSH