Real-time brain machine interaction via social robot gesture control

Reza Abiri, Soheil Borhani, Xiaopeng Zhao, Yang Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationAerospace 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
ISBN (Electronic)9780791858271
DOIs
StatePublished - 2017
EventASME 2017 Dynamic Systems and Control Conference, DSCC 2017 - Tysons, United States
Duration: Oct 11 2017Oct 13 2017

Publication series

NameASME 2017 Dynamic Systems and Control Conference, DSCC 2017
Volume1

Conference

ConferenceASME 2017 Dynamic Systems and Control Conference, DSCC 2017
Country/TerritoryUnited States
CityTysons
Period10/11/1710/13/17

Bibliographical note

Funding 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.

Publisher Copyright:
Copyright © 2017 ASME.

Keywords

  • Brain Computer Interface
  • Human-robot interaction
  • Motor imagery
  • Neurofeedback
  • Robot control
  • Social robot

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

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