Abstract

<p dir="ltr"><span>Magnetoencephalography (MEG) has become a pioneering technology in Brain-Computer Interfaces (BCIs) for neurorehabilitation, which significantly improves communication and motor rehabilitation for people with neurological conditions, especially stroke survivors. MEG-based BCIs allow users to regain control over their motor and cognitive functions by providing precise temporal and spatial resolution for detecting neural activity. The MEG has several benefits including reduced distortion from the skull and scalp, its non-invasive nature, and the ability to capture deep brain activity, all of which enhance the effectiveness of BCI systems. This study focuses on classifying motor and cognitive tasks using various models, including Recurrent Neural Networks (RNNs), one-dimensional Convolutional Neural Networks (1DCNNs), and a hybrid approach. The effectiveness of the models in classification tasks was evaluated using various metrics such as accuracy, recall, precision, and F1-score. Significantly, the hybrid model exhibited enhanced performance relative to the other models, achieving marked improvements in both classification accuracy and robustness. These results underscore the promise of MEG BCI technology for neurorehabilitation and stress the need for the development of sophisticated classification models to support the recovery of motor and cognitive abilities in people with neurological conditions. This study adds to the expanding research focused on enhancing the quality of life for affected individuals by utilizing innovative BCI solutions, leveraging the unique capabilities of MEG technology to enable more effective neurorehabilitation interventions.</span></p>