relation: http://repositorio.unib.org/id/eprint/14339/
canonical: http://repositorio.unib.org/id/eprint/14339/
title: EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning
creator: Kumari, Annu
creator: Edla, Damodar Reddy
creator: Reddy, R. Ravinder
creator: Jannu, Srikanth
creator: Vidyarthi, Ankit
creator: Alkhayyat, Ahmed
creator: Garat de Marin, Mirtha Silvana
subject: Biomedicina
subject: Ingeniería
description: Brain–computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model’s overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments
date: 2024-07
type: Artículo
type: PeerReviewed
identifier:   Artículo Materias > Biomedicina <http://repositorio.unib.org/view/subjects/uneat=5Fbm.html> Materias > Ingeniería <http://repositorio.unib.org/view/subjects/uneat=5Feng.html> Universidad Europea del Atlántico > Investigación > Producción Científica <http://repositorio.unib.org/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html> Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros <http://repositorio.unib.org/view/divisions/uninipr=5Fproduccion=5Fcientifica.html> Universidad Internacional do Cuanza > Investigación > Producción Científica <http://repositorio.unib.org/view/divisions/unic=5Fproduccion=5Fcientifica.html> Cerrado Inglés Brain–computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model’s overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments metadata Kumari, Annu; Edla, Damodar Reddy; Reddy, R. Ravinder; Jannu, Srikanth; Vidyarthi, Ankit; Alkhayyat, Ahmed y Garat de Marin, Mirtha Silvana mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, silvana.marin@uneatlantico.es       (2024) EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning.  Journal of Neuroscience Methods, 409.  p. 110215.  ISSN 01650270     
relation: http://doi.org/10.1016/j.jneumeth.2024.110215
relation: doi:10.1016/j.jneumeth.2024.110215
language: en