Semiautomatic physiologically-driven feature selection improves the usability of a brain computer interface system in post-stroke motor rehabilitation
In an electroencephalographic (EEG)-based BCI-assisted Motor Imagery (MI) training the reinforcement of a specific EEG pattern elicited by correct MI requires that expert neurophysiologists with knowledge of BCI technology identify the optimal control features for each single patient. This procedure is highly dependent on the operator and is currently restricted to