@article{unic14339,
          volume = {409},
           pages = {110215},
           title = {EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning},
           month = {Julio},
         journal = {Journal of Neuroscience Methods},
          author = {Annu Kumari and Damodar Reddy Edla and R. Ravinder Reddy and Srikanth Jannu and Ankit Vidyarthi and Ahmed Alkhayyat and Mirtha Silvana Garat de Marin},
            year = {2024},
        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},
             url = {http://repositorio.unic.co.ao/id/eprint/14339/}
}