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Imaginary Finger Movements Decoding Using Empirical Mode Decomposition and a Stacked BiLSTM Architecture

Author

Listed:
  • Tat’y Mwata-Velu

    (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)

  • Jorge Mario Cruz-Duarte

    (Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Col. Tecnológico, Monterrey 64849, Mexico)

  • Horacio Rostro-Gonzalez

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

  • Jose Ruiz-Pinales

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

Abstract

Motor Imagery Electroencephalogram (MI-EEG) signals are widely used in Brain-Computer Interfaces (BCI). MI-EEG signals of large limbs movements have been explored in recent researches because they deliver relevant classification rates for BCI systems. However, smaller and noisy signals corresponding to hand-finger imagined movements are less frequently used because they are difficult to classify. This study proposes a method for decoding finger imagined movements of the right hand. For this purpose, MI-EEG signals from C3, Cz, P3, and Pz sensors were carefully selected to be processed in the proposed framework. Therefore, a method based on Empirical Mode Decomposition (EMD) is used to tackle the problem of noisy signals. At the same time, the sequence classification is performed by a stacked Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed method was evaluated using k-fold cross-validation on a public dataset, obtaining an accuracy of 82.26%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3297-:d:705499
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    Cited by:

    1. 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.

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