EEG signal classification for wheelchair control application

Brain–Computer Interface (BCI) requires generating control signals for external device by analyzing and processing the internal brain signal. Person with severe impairment or spinal cord injury has loss of ability to do anything. This project about the EEG signals classification for wheelchair contr...

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Bibliographic Details
Main Author:	Abu Hassan, Rozi Roslinda
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Online Access:	http://eprints.uthm.edu.my/1448/1/ROZI%20ROSLINDA%20ABU%20HASSAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1448/2/24p%20ROZI%20ROSLINDA%20ABU%20HASSAN.pdf http://eprints.uthm.edu.my/1448/3/ROZI%20ROSLINDA%20ABU%20HASSAN%20WATERMARK.pdf
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id	my-uthm-ep.1448
record_format	uketd_dc
spelling	my-uthm-ep.14482021-10-03T07:24:06Z EEG signal classification for wheelchair control application 2015-01 Abu Hassan, Rozi Roslinda TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Brain–Computer Interface (BCI) requires generating control signals for external device by analyzing and processing the internal brain signal. Person with severe impairment or spinal cord injury has loss of ability to do anything. This project about the EEG signals classification for wheelchair control application. In this project, the movement of wheelchair (left, right, forward and reverse) will classified by user focusing based on four visible picture in various shape and colour also four non-visible picture (used thought image) that represent the movement. EEG signal were analyzed to find out the features by using Fast Fourier Transform (FFT). This project used alpha and beta band to collect the data. The analysis have made based on the peak and average value which then be compared to define the most significant differentiation between signals. From the result, shows that the visible colour model meet the most significant value based on the higher percentage than the other two models. 2015-01 Thesis http://eprints.uthm.edu.my/1448/ http://eprints.uthm.edu.my/1448/1/ROZI%20ROSLINDA%20ABU%20HASSAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1448/2/24p%20ROZI%20ROSLINDA%20ABU%20HASSAN.pdf text en public http://eprints.uthm.edu.my/1448/3/ROZI%20ROSLINDA%20ABU%20HASSAN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Abu Hassan, Rozi Roslinda EEG signal classification for wheelchair control application
description	Brain–Computer Interface (BCI) requires generating control signals for external device by analyzing and processing the internal brain signal. Person with severe impairment or spinal cord injury has loss of ability to do anything. This project about the EEG signals classification for wheelchair control application. In this project, the movement of wheelchair (left, right, forward and reverse) will classified by user focusing based on four visible picture in various shape and colour also four non-visible picture (used thought image) that represent the movement. EEG signal were analyzed to find out the features by using Fast Fourier Transform (FFT). This project used alpha and beta band to collect the data. The analysis have made based on the peak and average value which then be compared to define the most significant differentiation between signals. From the result, shows that the visible colour model meet the most significant value based on the higher percentage than the other two models.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Abu Hassan, Rozi Roslinda
author_facet	Abu Hassan, Rozi Roslinda
author_sort	Abu Hassan, Rozi Roslinda
title	EEG signal classification for wheelchair control application
title_short	EEG signal classification for wheelchair control application
title_full	EEG signal classification for wheelchair control application
title_fullStr	EEG signal classification for wheelchair control application
title_full_unstemmed	EEG signal classification for wheelchair control application
title_sort	eeg signal classification for wheelchair control application
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Electrical and Electronic Engineering
publishDate	2015
url	http://eprints.uthm.edu.my/1448/1/ROZI%20ROSLINDA%20ABU%20HASSAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1448/2/24p%20ROZI%20ROSLINDA%20ABU%20HASSAN.pdf http://eprints.uthm.edu.my/1448/3/ROZI%20ROSLINDA%20ABU%20HASSAN%20WATERMARK.pdf
_version_	1747830792026849280

EEG signal classification for wheelchair control application

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