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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Institute of Electrical and Electronics Engineers (IEEE)
2014
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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) |
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Brain EEG Interface Signals |
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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 |