Brain-Machine Interaction (BMI) system motivates interesting and promising results in forward/feedback control consistent with human intention. It holds great promise for advancements in patient care and applications to neurorehabilitation. Here, we propose a novel neurofeedbackbased BCI robotic platform using a personalized social robot in order to assist patients having cognitive deficits through bilateral rehabilitation and mental training. For initial testing of the platform, electroencephalography (EEG) brainwaves of a human user were collected in real time during tasks of imaginary movements. First, the brainwaves associated with imagined body kinematics parameters were decoded to control a cursor on a computer screen in training protocol. Then, the experienced subject was able to interact with a social robot via our real-time BMI robotic platform. Corresponding to subject's imagery performance, he/she received specific gesture movements and eye color changes as neural-based feedback from the robot. This hands-free neurofeedback interaction not only can be used for mind control of a social robot's movements, but also sets the stage for application to enhancing and recovering mental abilities such as attention via training in humans by providing real-time neurofeedback from a social robot.
|Title of host publication||Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems|
|State||Published - 2017|
|Event||ASME 2017 Dynamic Systems and Control Conference, DSCC 2017 - Tysons, United States|
Duration: Oct 11 2017 → Oct 13 2017
|Name||ASME 2017 Dynamic Systems and Control Conference, DSCC 2017|
|Conference||ASME 2017 Dynamic Systems and Control Conference, DSCC 2017|
|Period||10/11/17 → 10/13/17|
Bibliographical noteFunding Information:
This work was in part supported by a NeuroNET seed grant to XZ; and in part by the NIH under grants NIH P30 AG028383 to the UK Sanders-Brown Center on Aging, and NIH NCRR UL1TR000117 to the UK Center for Clinical and Translational Science. The authors are grateful for useful discussions of Dr. Nancy Munro.
Copyright © 2017 ASME.
- Brain Computer Interface
- Human-robot interaction
- Motor imagery
- Robot control
- Social robot
ASJC Scopus subject areas
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
- Mechanical Engineering