Clinical Research Directory

Participants will be seated wearing a cap on their head containing surface electrodes connected to a computer/laptop. The laptop employs machine learning algorithms and uses the scalp electroencephalogram (EEG) signals from the scalp's surface to control a robotic arm which supports the patient's stroke-affected arm. The machine learning algorithms rapidly interpret EEG signals and adjust the outgoing movement commands to the robot based on an individual's responses to stimuli. The participant will be wearing a VR headset. The VR displays visual stimulus in time with the robotic arm. Based on information from the EEG, the participant is able to animate their robotic arm and receive visual task-based feedback in the VR.

The study therapist will focus on 3 activities (\~15 minutes each/session): passive range of motion exercises, motor imagery, and mirror therapy. These activities were chosen as they are all passive activities that are commonly used in a patient with severe impairment of the post-stroke arm and hand. Each session will last \~1hour including set up and switching between activities.