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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| Format: | Thesis |
| Language: | English English English |
| Published: |
2015
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| Online Access: | http://eprints.uthm.edu.my/1448/ 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_ | 1848887463325466624 |
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| author | Abu Hassan, Rozi Roslinda |
| author_facet | Abu Hassan, Rozi Roslinda |
| author_sort | Abu Hassan, Rozi Roslinda |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T19:54:47Z |
| format | Thesis |
| id | uthm-1448 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-15T19:54:47Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-14482021-10-03T07:24:06Z http://eprints.uthm.edu.my/1448/ EEG signal classification for wheelchair control application 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 NonPeerReviewed text en http://eprints.uthm.edu.my/1448/1/ROZI%20ROSLINDA%20ABU%20HASSAN%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/1448/2/24p%20ROZI%20ROSLINDA%20ABU%20HASSAN.pdf text en http://eprints.uthm.edu.my/1448/3/ROZI%20ROSLINDA%20ABU%20HASSAN%20WATERMARK.pdf Abu Hassan, Rozi Roslinda (2015) EEG signal classification for wheelchair control application. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Abu Hassan, Rozi Roslinda EEG signal classification for wheelchair control application |
| title | 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_short | EEG signal classification for wheelchair control application |
| title_sort | eeg signal classification for wheelchair control application |
| topic | TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television |
| url | http://eprints.uthm.edu.my/1448/ 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 |