eprintid: 14339 rev_number: 7 eprint_status: archive userid: 2 dir: disk0/00/01/43/39 datestamp: 2024-09-23 23:30:12 lastmod: 2024-09-23 23:30:12 status_changed: 2024-09-23 23:30:12 type: article metadata_visibility: show creators_name: Kumari, Annu creators_name: Edla, Damodar Reddy creators_name: Reddy, R. Ravinder creators_name: Jannu, Srikanth creators_name: Vidyarthi, Ankit creators_name: Alkhayyat, Ahmed creators_name: Garat de Marin, Mirtha Silvana creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: silvana.marin@uneatlantico.es title: EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning ispublished: pub subjects: uneat_bm subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: none abstract: 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 publication: Journal of Neuroscience Methods volume: 409 pagerange: 110215 id_number: doi:10.1016/j.jneumeth.2024.110215 refereed: TRUE issn: 01650270 official_url: http://doi.org/10.1016/j.jneumeth.2024.110215 access: close language: en citation: Artículo Materias > Biomedicina Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica 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