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EEG Feature Extraction Using Evolutionary Algorithms for Brain-Computer Interface Development.
Rocha-Herrera, César Alfredo; Díaz-Manríquez, Alan; Barron-Zambrano, Jose Hugo; Elizondo-Leal, Juan Carlos; Saldivar-Alonso, Vicente Paul; Martínez-Angulo, Jose Ramon; Nuño-Maganda, Marco Aurelio; Polanco-Martagon, Said.
Afiliação
  • Rocha-Herrera CA; Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.
  • Díaz-Manríquez A; Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.
  • Barron-Zambrano JH; Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.
  • Elizondo-Leal JC; Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.
  • Saldivar-Alonso VP; Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.
  • Martínez-Angulo JR; Facultad de Ingeniería y Ciencias, Universidad Autonoma de Tamaulipas, Ciudad Victoria 87000, Tamaulipas, Mexico.
  • Nuño-Maganda MA; Intelligent Systems Department, Polytechnic University of Victoria, Ciudad Victoria 87138, Tamaulipas, Mexico.
  • Polanco-Martagon S; Intelligent Systems Department, Polytechnic University of Victoria, Ciudad Victoria 87138, Tamaulipas, Mexico.
Comput Intell Neurosci ; 2022: 7571208, 2022.
Article em En | MEDLINE | ID: mdl-35814562
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Interfaces Cérebro-Computador Tipo de estudo: Prognostic_studies Idioma: En Revista: Comput Intell Neurosci Assunto da revista: INFORMATICA MEDICA / NEUROLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: México País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Interfaces Cérebro-Computador Tipo de estudo: Prognostic_studies Idioma: En Revista: Comput Intell Neurosci Assunto da revista: INFORMATICA MEDICA / NEUROLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: México País de publicação: Estados Unidos