Welcome to the InfoSci Platform
Reference Hub4
An EEG-Based BCI Platform to Improve Arm Reaching Ability of Chronic Stroke Patients by Means of an Operant Learning Training with a Contingent Force Feedback

An EEG-Based BCI Platform to Improve Arm Reaching Ability of Chronic Stroke Patients by Means of an Operant Learning Training with a Contingent Force Feedback

Giulia Cisotto, Silvano Pupolin, Marianna Cavinato, Francesco Piccione
Copyright: © 2014 |Volume: 5 |Issue: 1 |Pages: 21
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781466653993|DOI: 10.4018/ijehmc.2014010107
Cite Article Cite Article

MLA

Cisotto, Giulia, et al. "An EEG-Based BCI Platform to Improve Arm Reaching Ability of Chronic Stroke Patients by Means of an Operant Learning Training with a Contingent Force Feedback." IJEHMC vol.5, no.1 2014: pp.114-134. http://doi.org/10.4018/ijehmc.2014010107

APA

Cisotto, G., Pupolin, S., Cavinato, M., & Piccione, F. (2014). An EEG-Based BCI Platform to Improve Arm Reaching Ability of Chronic Stroke Patients by Means of an Operant Learning Training with a Contingent Force Feedback. International Journal of E-Health and Medical Communications (IJEHMC), 5(1), 114-134. http://doi.org/10.4018/ijehmc.2014010107

Chicago

Cisotto, Giulia, et al. "An EEG-Based BCI Platform to Improve Arm Reaching Ability of Chronic Stroke Patients by Means of an Operant Learning Training with a Contingent Force Feedback," International Journal of E-Health and Medical Communications (IJEHMC) 5, no.1: 114-134. http://doi.org/10.4018/ijehmc.2014010107

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The Brain Computer Interface platform described in this paper was implemented to enhance neuroplasticity of a stroke-damaged brain in order to promote recovery of motor functions like reaching, fundamentally important in a healthy daily life. To this scope a closed-loop between the stroke patients' brain and a robotic arm is established by means of a real-time identification of the cerebral activity related to the movement and its transformation in a force feedback delivered by the robot. In particular, an operant-learning strategy is employed: while patients are performing the motor task they receive a feedback of their neural activity. If the latter agrees with the expected neurophysiological hypothesis, they are helped by the robotic arm in completing the task. The method trains patients to control the modulation of sensorimotor rhythms of their perilesional area and, at the same time, it should induce them to associate that modulation to the reaching movement. In this way, the modification of the neural activity becomes an alternative tool for controlling the impaired reaching ability bypassing the damaged brain area. Preliminary encouraging results were found in both the two first patients recruited in the program.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.