TY  - JOUR
AV  - none
JF  - Journal of Neuroscience Methods
ID  - uneatlantico14339
N2  - 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
TI  - EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning
Y1  - 2024/07//
UR  - http://doi.org/10.1016/j.jneumeth.2024.110215
SN  - 01650270
VL  - 409
A1  - Kumari, Annu
A1  - Edla, Damodar Reddy
A1  - Reddy, R. Ravinder
A1  - Jannu, Srikanth
A1  - Vidyarthi, Ankit
A1  - Alkhayyat, Ahmed
A1  - Garat de Marin, Mirtha Silvana
ER  -