%0 Journal Article
%@ 01650270
%A Kumari, Annu
%A Edla, Damodar Reddy
%A Reddy, R. Ravinder
%A Jannu, Srikanth
%A Vidyarthi, Ankit
%A Alkhayyat, Ahmed
%A Garat de Marin, Mirtha Silvana
%D 2024
%F uneatlantico:14339
%J Journal of Neuroscience Methods
%P 110215
%T EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning
%U http://repositorio.uneatlantico.es/id/eprint/14339/
%V 409
%X 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