Next Article in Journal
Transformation Method for Solving System of Boolean Algebraic Equations
Previous Article in Journal
Assessment of Complex System Dynamics via Harmonic Mapping in a Multifractal Paradigm
Previous Article in Special Issue
Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Reduction Mapping
 
 
Article

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

1
Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, University of Guanajuato, Salamanca 36885, Mexico
2
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)
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) Electroencephalogram (EEG); Motor Imagery (MI); Empirical Mode Decomposition (EMD); Bidirectional Long Short-Term Memory (BiLSTM)
Show Figures

Figure 1

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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop