Brain Computer Interface for Gesture Control of a Social Robot: an Offline Study
This work addresses gesture control for social robots to assist persons with dementia, though it is an incremental advance in BCI-human-robot interaction.
The researchers developed a brain-computer interface (BCI) platform that decodes imagined movements from EEG signals to control a social robot's gestures, achieving real-time decoding of user-intended velocity for potential applications in neurorehabilitation.
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.