An EEG-based BCI platform to improve arm reaching ability of chronic stroke patients by means of an operant learning training with a contingent force feedback

The Brain Computer Interface platform described in this paper was implemented to enhance neuroplasticity of a stroke-damaged brain in order to promote recovery of motor functions like reaching, fundamentally important in a healthy daily life. To this scope a closed-loop between the stroke patients' brain and a robotic arm is established by means of a real-time identification of the cerebral activity related to the movement and its transformation in a force feedback delivered by the robot. In particular, an operant-learning strategy is employed: while patients are performing the motor task they receive a feedback of their neural activity. If the latter agrees with the expected neurophysiological hypothesis, they are helped by the robotic arm in completing the task. The method trains patients to control the modulation of sensorimotor rhythms of their perilesional area and, at the same time, it should induce them to associate that modulation to the reaching movement. In this way, the modification of the neural activity becomes an alternative tool for controlling the impaired reaching ability bypassing the damaged brain area. Preliminary encouraging results were found in both the two first patients recruited in the program.