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
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/57005/ http://irep.iium.edu.my/57005/ http://irep.iium.edu.my/57005/1/57005_Brain%20Computer%20Interface.pdf

id	iium-57005
recordtype	eprints
spelling	iium-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 PeerReviewed 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/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
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/ http://irep.iium.edu.my/57005/ http://irep.iium.edu.my/57005/1/57005_Brain%20Computer%20Interface.pdf
first_indexed	2023-09-18T21:20:31Z
last_indexed	2023-09-18T21:20:31Z
_version_	1777411850464395264

Brain computer interface based wheelchair for disable people using electroencephalography signal

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