EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning.

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.

An Adaptive, Interacting, Cluster-Based Model Accurately Predicts the Transmission Dynamics of COVID-19

The SARS-CoV-2 driven disease, COVID-19, is presently a pandemic with increasing human and monetary costs. COVID-19 has put an unexpected and inordinate degree of pressure on healthcare systems of strong and fragile countries alike. In order to launch both containment and mitigation measures, each country requires accurate estimates of COVID-19 incidence as such preparedness allows agencies to plan efficient resource allocation and design control strategies. Here, we have developed a new adaptive, interacting, and cluster-based mathematical model to predict the granular trajectory COVID-19. We have analyzed incidence data from three currently afflicted countries of Italy, the United States of America, and India, and show that our approach predicts state-wise COVID-19 spread for each country with high accuracy. We show that R0 as the basic reproduction number exhibits significant spatial and temporal variation in these countries. However, by including a new function for temporal variation of R0 in an adaptive fashion, the predictive model provides highly reliable estimates of asymptomatic and undetected COVID-19 patients, both of which are key players in COVID-19 transmission. Our dynamic modeling approach can be applied widely and will provide a new fillip to infectious disease management strategies worldwide.