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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Main Author: Abu Hassan, Rozi Roslinda
Format: Thesis
Language:English
English
English
Published: 2015
Subjects:
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
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