Implementation of Artificial Communication Channel for Motor Patients

Priyanka Chaturvedi , Silky Pareyani

Brain Computer Interface (BCI), Electroencephalogram (EEG), Hilbert Huang Transform (HHT), Support Vector Machine (SVM), Classification

Brain Computer Interface establishes a direct communication pathway between human brain and outside world. This capability of Brain Computer Interface system enables patients suffering from severe motor disorder or complete body paralysis to control variety of applications like, controlling a cursor on computer screen, controlling the movement of a robotic or a wheel arm chair and many more. The efficiency of a Brain Computer Interface system completely rely on efficient pre-processing and classification algorithms. In present work, a methodology of feature extraction and classification of Electroencephalogram signals is proposed for implementation of Brain Computer Interface for physically disabled. The EEG signal under consideration has been recorded from seven different subjects performing five different mental tasks. Time Frequency Energy Distribution spectrum is computed from the coefficient obtained from Hilbert Huang transform of Electroencephalogram signals. Four statistical parameters are calculated from the TFED of signals as features. For classification, Support Vector Machine classifier model is employed. The results of the classification show efficacy of present methodology of feature extraction and classification for implementation of Brain Computer Interface.

Published Paper   E-Certificate

"Implementation of Artificial Communication Channel for Motor Patients", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.3, Issue 11, page no.9 - 17, December-2018, Available :https://ijsdr.org/papers/IJSDR1811003.pdf

Volume 3 Issue 11, December-2018

Pages : 9 - 17

Paper Reg. ID: IJSDR_180723

Published Paper Id: IJSDR1811003

Downloads: 000347175

Research Area: Engineering

Country: Rewa, mp, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR1811003

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR1811003

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition