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

Mwata-Velu, Tat’y; Avina-Cervantes, Juan Gabriel; Cruz-Duarte, Jorge Mario; Rostro-Gonzalez, Horacio; Ruiz-Pinales, Jose

doi:10.3390/math9243297

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

Tat’y Mwata-Velu

Juan Gabriel Avina-Cervantes

Jorge Mario Cruz-Duarte

Horacio Rostro-Gonzalez

and

Jose Ruiz-Pinales

^1,*

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

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

Author to whom correspondence should be addressed.

Academic Editor: Miguel Atencia

Mathematics 2021, 9(24), 3297; https://doi.org/10.3390/math9243297

Received: 26 October 2021 / Revised: 7 December 2021 / Accepted: 16 December 2021 / Published: 18 December 2021

(This article belongs to the Special Issue Numerical Analysis of Artificial Neural Networks)

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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%. View Full-Text

Keywords: Electroencephalogram (EEG); Motor Imagery (MI); Empirical Mode Decomposition (EMD); Bidirectional Long Short-Term Memory (BiLSTM)

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MDPI and ACS Style

Mwata-Velu, T.; Avina-Cervantes, J.G.; Cruz-Duarte, J.M.; Rostro-Gonzalez, H.; Ruiz-Pinales, J. Imaginary Finger Movements Decoding Using Empirical Mode Decomposition and a Stacked BiLSTM Architecture. Mathematics 2021, 9, 3297. https://doi.org/10.3390/math9243297

AMA Style

Mwata-Velu T, Avina-Cervantes JG, Cruz-Duarte JM, Rostro-Gonzalez H, Ruiz-Pinales J. Imaginary Finger Movements Decoding Using Empirical Mode Decomposition and a Stacked BiLSTM Architecture. Mathematics. 2021; 9(24):3297. https://doi.org/10.3390/math9243297

Chicago/Turabian Style

Mwata-Velu, Tat’y, Juan Gabriel Avina-Cervantes, Jorge Mario Cruz-Duarte, Horacio Rostro-Gonzalez, and Jose Ruiz-Pinales. 2021. "Imaginary Finger Movements Decoding Using Empirical Mode Decomposition and a Stacked BiLSTM Architecture" Mathematics 9, no. 24: 3297. https://doi.org/10.3390/math9243297

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

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