EEGEncoder: Advancing BCI with Transformer-Based Motor Imagery Classification

Brain-computer interfaces (BCIs) represent a cutting-edge technological frontier, offering a transformative approach to human-computer interaction. By facilitating direct neural communication, BCIs enable individuals to control external devices or systems through cerebral activity alone, bypassing conventional motor pathways.BCIs are particularly promising for applications in healthcare, rehabilitation, entertainment, and education. In the medical field, they provide a glimmer of hope for individuals with motor impairments, enabling the restoration of control over bodily functions. For example, BCIs have been instrumental in assisting individuals with spinal cord injuries to operate prosthetic limbs and have aided stroke survivors in regaining mobility [1, 2]. A critical BCI modality is EEG-based motor imagery (MI), which utilizes electroencephalographic (EEG) signals to deduce a user's intent for limb movement.