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EEG-BCI Features Discrimination between Executed and Imagined Movements Based on FastICA, Hjorth Parameters, and SVM

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  • Tat’y Mwata-Velu

    (Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Avenida Juan de Dios Bátiz esquina Miguel Othón de Mendizábal Colonia Nueva Industrial Vallejo, Alcadía Gustavo A. Madero, Ciudad de México 07738, Mexico
    Institut Supérieur Pédagogique Technique de Kinshasa (ISPT-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo
    Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, University of Guanajuato, Salamanca 36885, Mexico)

  • Armando Navarro Rodríguez

    (Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Avenida Juan de Dios Bátiz esquina Miguel Othón de Mendizábal Colonia Nueva Industrial Vallejo, Alcadía Gustavo A. Madero, Ciudad de México 07738, Mexico)

  • Yanick Mfuni-Tshimanga

    (Institut Supérieur des Techniques Appliquées (ISTA-NDOLO), Avenue de l’aérodrome, Kinshasa 6593, Democratic Republic of the Congo)

  • Richard Mavuela-Maniansa

    (Institut Supérieur Pédagogique Technique de Kinshasa (ISPT-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo)

  • Jesús Alberto Martínez Castro

    (Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Avenida Juan de Dios Bátiz esquina Miguel Othón de Mendizábal Colonia Nueva Industrial Vallejo, Alcadía Gustavo A. Madero, Ciudad de México 07738, Mexico)

  • Jose Ruiz-Pinales

    (Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, University of Guanajuato, Salamanca 36885, Mexico)

  • Juan Gabriel Avina-Cervantes

    (Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, University of Guanajuato, Salamanca 36885, Mexico)

Abstract

Brain–Computer Interfaces (BCIs) communicate between a given user and their nearest environment through brain signals. In the case of device handling, an accurate control-based BCI depends essentially on how the user performs corresponding mental tasks. In the BCI illiteracy-related literature, one subject could perform a defined paradigm better than another. Therefore, this work aims to identify recorded Electroencephalogram (EEG) signal segments related to the executed and imagined motor tasks for BCI system applications. The proposed approach implements pass-band filters and the Fast Independent Component Analysis (FastICA) algorithm to separate independent sources from raw EEG signals. Next, EEG features of selected channels are extracted using Hjorth parameters. Finally, a Support Vector Machines (SVMs)-based classifier identifies executed and imagined motor features. Concretely, the Physionet dataset, related to executed and imagined motor EEG signals, provided training, testing, and validating data. The numerical results let us discriminate between executed and imagined motor tasks accurately. Therefore, the proposed method offers a reliable alternative to extract EEG features for BCI based on executed and imagined movements.

Suggested Citation

  • Tat’y Mwata-Velu & Armando Navarro Rodríguez & Yanick Mfuni-Tshimanga & Richard Mavuela-Maniansa & Jesús Alberto Martínez Castro & Jose Ruiz-Pinales & Juan Gabriel Avina-Cervantes, 2023. "EEG-BCI Features Discrimination between Executed and Imagined Movements Based on FastICA, Hjorth Parameters, and SVM," Mathematics, MDPI, vol. 11(21), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4409-:d:1266473
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    References listed on IDEAS

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    1. Baravalle, Roman & Rosso, Osvaldo A. & Montani, Fernando, 2018. "Discriminating imagined and non-imagined tasks in the motor cortex area: Entropy-complexity plane with a wavelet decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 27-39.
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