Motor Imagery Classification

Motor imagery classification aims to decode imagined movements from brain signals (e.g., EEG) to control external devices, primarily for brain-computer interfaces (BCIs). Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs), often incorporating techniques like Riemannian geometry and domain adaptation to address inter-subject variability and improve classification accuracy. This field is crucial for advancing BCIs, offering potential for assistive technologies and rehabilitation by enabling intuitive control of prosthetics and other devices for individuals with motor impairments.