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
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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

Mathematics, 2021, vol. 9, issue 24, 1-14

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%.

Keywords: Electroencephalogram (EEG); Motor Imagery (MI); Empirical Mode Decomposition (EMD); Bidirectional Long Short-Term Memory (BiLSTM) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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