Classification of Motor Imagery EEG based Tasks for Brain Computer Interfacing Applications

This project focuses on classifying single-joint motor imagery (MI) EEG signals for applications in Brain-Computer-Interfacing(BCI). By leveraging deep learning models—including CNNs, Transformers, and hybrid architectures—we aim to improve the usability of MI-based single-joint classification.