Brain Computer Interface for Gesture Control of a Social Robot: an Offline Study

TL;DR

This study introduces a novel noninvasive BCI system that decodes imagined movements from EEG signals to control a social robot's gestures, aiming to enhance human-robot interaction and cognitive rehabilitation.

Contribution

The paper presents a new BCI platform that translates EEG-based imagined movement signals into robot gestures, integrating neurofeedback for potential cognitive therapy.

Findings

Successful decoding of imagined movements from EEG signals.

Effective control of social robot gestures via BCI.

Potential applications in neurorehabilitation and dementia care.

Abstract

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.