Brain computer interface based wheelchair for disable people using electroencephalography signal

Brain computer interface causes direct operation between brain and computer. Interfacing of the EEG signal produced by the brain with any control or communication device produces unidirectional communicating channel. Among the non-invasive techniques for probing human brain dynamics, EEG provides a...

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Main Author: Ibrahimy, Muhammad Ibn
Format: Conference or Workshop Item
Language:English
Published: Zes Rokman Resources 2017
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Online Access:http://irep.iium.edu.my/57005/1/57005_Brain%20Computer%20Interface.pdf
http://irep.iium.edu.my/57005/
http://www.piccwed.com/
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spelling my.iium.irep.570052017-12-30T09:31:12Z http://irep.iium.edu.my/57005/ Brain computer interface based wheelchair for disable people using electroencephalography signal Ibrahimy, Muhammad Ibn T Technology (General) Brain computer interface causes direct operation between brain and computer. Interfacing of the EEG signal produced by the brain with any control or communication device produces unidirectional communicating channel. Among the non-invasive techniques for probing human brain dynamics, EEG provides a direct measurement of cortical activity i.e., intention of a human being; with millisecond temporal resolution. However, the well-off interconnectivity between the various cortical areas may allow for events in one area to be preceded or accompanied by detectable patterns in other unrelated areas. To develop a practical BCI system, three components should be considered. These are i) to establish an appropriate multivariate signal processing technique to extract multiclass features from multi-channel EEG signals, ii) to look up suitable pattern classification technique to improve the performance of BCI and finally iii) to develop an approprite interfacing circuit to control a user device. Due to poor classification acuracy, practical BCI system has not been fully materialised yet. However, an advanced and simple classification algorithm for motor imagery related BCI system has already been developed with Mahalanobis Discriminant Analysis (MDA) technique. It obtains 93% of kappa accuracy in evaluation phase, which is validated and acceptable, whereas the accuracy with others is maxmimum 86%. Moreover, the developed technique needs a very low computational requirement that makes it suitable for real-time BCI based system to control a wheelchair for the disabled people. To have a fruitful result, the next phase of hardware realization research and interfacing with users are essential which is highly desired factor in a practical/commercial BCI system development. Zes Rokman Resources 2017-05-20 Conference or Workshop Item REM application/pdf en http://irep.iium.edu.my/57005/1/57005_Brain%20Computer%20Interface.pdf Ibrahimy, Muhammad Ibn (2017) Brain computer interface based wheelchair for disable people using electroencephalography signal. In: 2nd Putrajaya International Conference on Children, Women, Elderly and Disabled People (PICCWED2), 20th-21st May 2017, Selangor, Malaysia. http://www.piccwed.com/
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Ibrahimy, Muhammad Ibn
Brain computer interface based wheelchair for disable people using electroencephalography signal
description Brain computer interface causes direct operation between brain and computer. Interfacing of the EEG signal produced by the brain with any control or communication device produces unidirectional communicating channel. Among the non-invasive techniques for probing human brain dynamics, EEG provides a direct measurement of cortical activity i.e., intention of a human being; with millisecond temporal resolution. However, the well-off interconnectivity between the various cortical areas may allow for events in one area to be preceded or accompanied by detectable patterns in other unrelated areas. To develop a practical BCI system, three components should be considered. These are i) to establish an appropriate multivariate signal processing technique to extract multiclass features from multi-channel EEG signals, ii) to look up suitable pattern classification technique to improve the performance of BCI and finally iii) to develop an approprite interfacing circuit to control a user device. Due to poor classification acuracy, practical BCI system has not been fully materialised yet. However, an advanced and simple classification algorithm for motor imagery related BCI system has already been developed with Mahalanobis Discriminant Analysis (MDA) technique. It obtains 93% of kappa accuracy in evaluation phase, which is validated and acceptable, whereas the accuracy with others is maxmimum 86%. Moreover, the developed technique needs a very low computational requirement that makes it suitable for real-time BCI based system to control a wheelchair for the disabled people. To have a fruitful result, the next phase of hardware realization research and interfacing with users are essential which is highly desired factor in a practical/commercial BCI system development.
format Conference or Workshop Item
author Ibrahimy, Muhammad Ibn
author_facet Ibrahimy, Muhammad Ibn
author_sort Ibrahimy, Muhammad Ibn
title Brain computer interface based wheelchair for disable people using electroencephalography signal
title_short Brain computer interface based wheelchair for disable people using electroencephalography signal
title_full Brain computer interface based wheelchair for disable people using electroencephalography signal
title_fullStr Brain computer interface based wheelchair for disable people using electroencephalography signal
title_full_unstemmed Brain computer interface based wheelchair for disable people using electroencephalography signal
title_sort brain computer interface based wheelchair for disable people using electroencephalography signal
publisher Zes Rokman Resources
publishDate 2017
url http://irep.iium.edu.my/57005/1/57005_Brain%20Computer%20Interface.pdf
http://irep.iium.edu.my/57005/
http://www.piccwed.com/
_version_ 1643615046250528768
score 13.211869