IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i13p2302-d853681.html
   My bibliography  Save this article

Improving Motor Imagery EEG Classification Based on Channel Selection Using a Deep Learning Architecture

Author

Listed:
  • Tat’y Mwata-Velu

    (Telematics and Digital Signal Processing Research Groups (CAs), Electronics Engineering Department, University of Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8, Com. Palo Blanco, Salamanca 36885, Mexico)

  • Juan Gabriel Avina-Cervantes

    (Telematics and Digital Signal Processing Research Groups (CAs), Electronics Engineering Department, University of Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8, Com. Palo Blanco, Salamanca 36885, Mexico)

  • Jose Ruiz-Pinales

    (Telematics and Digital Signal Processing Research Groups (CAs), Electronics Engineering Department, University of Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8, Com. Palo Blanco, Salamanca 36885, Mexico)

  • Tomas Alberto Garcia-Calva

    (Telematics and Digital Signal Processing Research Groups (CAs), Electronics Engineering Department, University of Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8, Com. Palo Blanco, Salamanca 36885, Mexico)

  • Erick-Alejandro González-Barbosa

    (Tecnológico Nacional de México/ITS de Irapuato, Carretera Irapuato—Silao km 12.5 Colonia El Copal, Irapuato 36821, Mexico)

  • Juan B. Hurtado-Ramos

    (Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada—Unidad Querétaro, Av. Cerro Blanco 141, Col. Colinas del Cimatario, Querétaro 76090, Mexico)

  • José-Joel González-Barbosa

    (Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada—Unidad Querétaro, Av. Cerro Blanco 141, Col. Colinas del Cimatario, Querétaro 76090, Mexico)

Abstract

Recently, motor imagery EEG signals have been widely applied in Brain–Computer Interfaces (BCI). These signals are typically observed in the first motor cortex of the brain, resulting from the imagination of body limb movements. For non-invasive BCI systems, it is not apparent how to locate the electrodes, optimizing the accuracy for a given task. This study proposes a comparative analysis of channel signals exploiting the Deep Learning (DL) technique and a public dataset to locate the most discriminant channels. EEG channels are usually selected based on the function and nomenclature of electrode location from international standards. Instead, the most suitable configuration for a given paradigm must be determined by analyzing the proper selection of the channels. Therefore, an EEGNet network was implemented to classify signals from different channel location using the accuracy metric. Achieved results were then contrasted with results from the state-of-the-art. As a result, the proposed method improved BCI classification accuracy.

Suggested Citation

  • Tat’y Mwata-Velu & Juan Gabriel Avina-Cervantes & Jose Ruiz-Pinales & Tomas Alberto Garcia-Calva & Erick-Alejandro González-Barbosa & Juan B. Hurtado-Ramos & José-Joel González-Barbosa, 2022. "Improving Motor Imagery EEG Classification Based on Channel Selection Using a Deep Learning Architecture," Mathematics, MDPI, vol. 10(13), pages 1-14, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2302-:d:853681
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/13/2302/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/13/2302/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tat’y Mwata-Velu & Juan Gabriel Avina-Cervantes & Jorge Mario Cruz-Duarte & Horacio Rostro-Gonzalez & Jose Ruiz-Pinales, 2021. "Imaginary Finger Movements Decoding Using Empirical Mode Decomposition and a Stacked BiLSTM Architecture," Mathematics, MDPI, vol. 9(24), pages 1-14, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nannan Xu & Xinze Cui & Xin Wang & Wei Zhang & Tianyu Zhao, 2022. "An Intelligent Athlete Signal Processing Methodology for Balance Control Ability Assessment with Multi-Headed Self-Attention Mechanism," Mathematics, MDPI, vol. 10(15), pages 1-16, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2302-:d:853681. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.