A Comparative Analysis for EEG-Based Motor Imagery Classification Across Diverse Datasets

The BRAIN CONTROL INTERFACE(BCI) introduce a different framework for human-computer interaction, enabling diverse applications across various fields. Motor imagery (MI) based BCI has found widespread application in assisting individuals with paralysis by controlling prosthetic limbs, smart homes, and even virtual reality experiences. Despite these ad-vancements, there are still limitations and obstacles to overcome before BCI technology can be widely adopted. This article focuses on a critical aspect of BCI development: accurately classifying MI tasks. The research focuses on the effectiveness of the EEGNet model for classifying MI signals. The findings highlight a significant difference in EEGNet's performance across various datasets on the BCI Competition IV. Notably, the model achieved accuracies of 85.74% and 69.68% for subject-specific scenarios on BCI-Competition IV-2b and IV-2a datasets, respectively.