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A Real-Time Brainwave Based Neuro-Feedback System for Cognitive Enhancement

[+] Author Affiliations
Reza Abiri, Joseph McBride, Xiaopeng Zhao

University of Tennessee, Knoxville, Knoxville, TN

Yang Jiang

University of Kentucky, College of Medicine, Lexington, KY

Paper No. DSCC2015-9855, pp. V001T16A005; 5 pages
doi:10.1115/DSCC2015-9855
From:
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5724-3
  • Copyright © 2015 by ASME

abstract

Brain Computer Interface (BCI) provides a pathway to connect the brain to external devices. Neuro-rehabilitation provides advanced means to assist people with movement disorders such as post-stroke patients and those with lost limbs. While much progress has been made in neuro-rehabilitation as assistive devices, few studies had examined mental rehabilitation assisted by BCI such as memory training using neuroenhancement. It should be noted that many patients with physical disabilities also suffer cognitive difficulties. On the other hand, cognitive decline can also be the result of normal aging without brain injury nor diseases. Here, we propose a novel real-time brainwave BCI platform for enhancing human cognitive by designing and employing a personalized neuro-feedback robot. Short-term memory and attention are among the most important cognitive abilities which manifest in many mental diseases. A social robot is integrated into the BCI system to provide feedback based on individual’s brainwaves and memory performance. As a simple scenario of memory task, real-time EEG signals will be monitored during a visual object memory task. Our novel neuro-feedback system has great potential as a neuro-enhancing device for cognitive rehabilitation.

Copyright © 2015 by ASME
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