Brain Computer Interface controlled Soft Finger Exoskeleton for Rehabilitation – Reality and Virtual Control Analysis

Authors:

Suresh. G,Vickneswari. D,N. Kok Sin,

DOI NO:

https://doi.org/10.26782/jmcms.spl.4/2019.11.00005

Keywords:

Exoskeleton,Rehabilitation,Brain Computer Interface,Real-time processing,Reality and Virtual Control,

Abstract

Brain Computer Interface (BCI) machine in this project is developed with Rehabilitation hand to enhance and amplify the motor function feedback for the subject to strengthen then connection between the muscle activation and brain activities in order to recover their paralyzed motor function. In this paper, the highlight will be on Reality and Virtual Control analysis of the BCI ma-chine accuracy in control for 10 different subjects. The classifiers LDA and ESD will be used in the BCI machine. The EEG coverage area is F7, F8, FC5, FC6, F3 and F4. The aim of the project is to have a system that is controlled by Electroencephalogram (EEG) BCI that improves Neuroplasticity Brain activation for Rehabilitation of Stroke Patient on Finger-hand paresis. The BCI analysis is focused on temporal information features extractions. The outcome of the project achieved overall control accuracy for manual control is 40% and for auto control is 30% in online BCI, which is promising.

Refference:

I. Ang, K. And Guan, C. (2013) Brain-Computer Interface in Stroke
Rehabilitation. Journal of Computing Science and Engineering. 7(2). p. 139-
146.
II. World Heart Federation (2017) World Heart Federation. [Online] Available
at: http://www.world-heart-federation.org/cardiovascular-health/stroke/
[Accessed 10 April 2017].
III. Shindo, K., Kawashima, K., Ushiba, J., Ota, N., Ito, M., Ota, T., Kimura, A.,
And Liu, M. (2011) Effects of Neurofeedback training with an
Electroencephalogram-Based-Brain-Computer Interface for Hand Paralysis in
Patients with Chronic Stroke: A Preliminary Case series study. J Rehabil
Med. p. 951-957.
IV. Lotte, F. (2014) Chapter 7: A Tutorial on EEG Signal Processing Techniques
for Mental State Recognition in Brain-Computer Interface. In: Guide to
Brain-Computer Music Interfacing. s.l.:Springer.Booklet A. (1994). Booklet
title. On the WWW, at http://www.abc.edu,May. PDF file.

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