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

Tat’y Mwata-Velu, Juan Gabriel Avina-Cervantes, Jose Ruiz-Pinales, Tomas Alberto Garcia-Calva, Erick-Alejandro González-Barbosa, Juan B. Hurtado-Ramos and José-Joel González-Barbosa
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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

Mathematics, 2022, vol. 10, issue 13, 1-14

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.

Keywords: motor imagery; EEG signals; deep learning; EEGNet; 10–20 international system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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