  Brain computer interface (BCI) provides promising applications in
neuroprosthesis and neurorehabilitation by controlling computers and robotic
devices based on the patient's intentions. Here, we have developed a novel BCI
platform that controls a personalized social robot using noninvasively acquired
brain signals. Scalp electroencephalogram (EEG) signals are collected from a
user in real-time during tasks of imaginary movements. The imagined body
kinematics are decoded using a regression model to calculate the user-intended
velocity. Then, the decoded kinematic information is mapped to control the
gestures of a social robot. The platform here may be utilized as a
human-robot-interaction framework by combining with neurofeedback mechanisms to
enhance the cognitive capability of persons with dementia.
