Adaptive Riemannian BCI for Enhanced Motor Imagery Training Protocols

Traditional methods of training a Brain-Computer Interface (BCI) on motor imagery (MI) data generally involve multiple intensive sessions. The initial sessions produce simple prompts to users, while later sessions additionally provide realtime feedback to users, allowing for human adaptation to take place. However, this protocol only permits the BCI to update between sessions, with little real-time evaluation of how the classifier has improved....