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Detection of wrist movement using EEG signal for brain machine interface

Proceeding of The International Conference on Technology, Informatics, Management, Engineering and Environment 2013 (TIME-E 2013) at Bandung, Indonesia on 23 June 2013 through 26 June 2013.

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Bibliographic Details
Main Authors: Farid, Ghani, Prof. Dr., Gaur, Bhoomika, Varshney, Sidhika, Farooq, Omar, Khan, Yusufuzzama
Other Authors: faridghani@unimap.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2014
Subjects:
Brain
EEG
Interface
Signals
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/34175
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spelling my.unimap-341752014-04-29T07:32:12Z Detection of wrist movement using EEG signal for brain machine interface Farid, Ghani, Prof. Dr. Gaur, Bhoomika Varshney, Sidhika Farooq, Omar Khan, Yusufuzzama faridghani@unimap.edu.my bhoomika.gaur117@gmail.com sidhika.varshney@gmail.com omar.farooq@amu.ac.in yusutkbanl@gmail.com Brain EEG Interface Signals Proceeding of The International Conference on Technology, Informatics, Management, Engineering and Environment 2013 (TIME-E 2013) at Bandung, Indonesia on 23 June 2013 through 26 June 2013. Brain machine interfaces (BMIs) allow patients suffering from neuromuscular disorders to control the movement of robotic limb or wheelchair under their own guidance. So far only invasive technologies e.g. Electrocorticography (ECoG) or intracranial EEG (iEEG) have been widely acknowledged in the design of BMIs. In this paper Electroencephalography (EEG), a non-invasive technology, has been used. The paper deals with study of the features of EEG signals corresponding to two different movements of human hand, namely flexion and extension. The movements have been detected on the basis of the energy and entropy of the corresponding signals. A total of twelve features have been used. Using different combinations of these features a surprisingly high accuracy of 87% has been obtained. Moreover, the use of only discrete cosine transformation of energy and entropy has yielded even a higher average accuracy of 91.93%. With such results, this wrist movement detection algorithm is successfully implemented on a robotic arm. 2014-04-29T07:32:12Z 2014-04-29T07:32:12Z 2013-06 Working Paper p. 5-8 978-146735732-6 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6611954 http://dspace.unimap.edu.my:80/dspace/handle/123456789/34175 en Proceeding of The International Conference on Technology, Informatics, Management, Engineering and Environment 2013 (TIME-E 2013); Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Brain
EEG
Interface
Signals
spellingShingle Brain
EEG
Interface
Signals
Farid, Ghani, Prof. Dr.
Gaur, Bhoomika
Varshney, Sidhika
Farooq, Omar
Khan, Yusufuzzama
Detection of wrist movement using EEG signal for brain machine interface
description Proceeding of The International Conference on Technology, Informatics, Management, Engineering and Environment 2013 (TIME-E 2013) at Bandung, Indonesia on 23 June 2013 through 26 June 2013.
author2 faridghani@unimap.edu.my
author_facet faridghani@unimap.edu.my
Farid, Ghani, Prof. Dr.
Gaur, Bhoomika
Varshney, Sidhika
Farooq, Omar
Khan, Yusufuzzama
format Working Paper
author Farid, Ghani, Prof. Dr.
Gaur, Bhoomika
Varshney, Sidhika
Farooq, Omar
Khan, Yusufuzzama
author_sort Farid, Ghani, Prof. Dr.
title Detection of wrist movement using EEG signal for brain machine interface
title_short Detection of wrist movement using EEG signal for brain machine interface
title_full Detection of wrist movement using EEG signal for brain machine interface
title_fullStr Detection of wrist movement using EEG signal for brain machine interface
title_full_unstemmed Detection of wrist movement using EEG signal for brain machine interface
title_sort detection of wrist movement using eeg signal for brain machine interface
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/34175
_version_ 1643797412815306752
score 13.214268

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