{"id":{"repo_id":"uthm","oai_identifier":"oai:eprints.uthm.edu.my:1448"},"canonical_url":"https://search.dev.ndltd.org/etd/uthm/oai:eprints.uthm.edu.my:1448","repository":{"repo_id":"uthm","name":"Universiti Tun Hussein Onn Malaysia","base_url":"http://eprints.uthm.edu.my/cgi/oai2"},"display":{"title":"EEG signal classification for wheelchair control application","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Abu Hassan, Rozi Roslinda"],"institution":"Universiti Tun Hussein Onn Malaysia","degree_name":"mphil","degree_level":"masters","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-01","date_published":"2015-01","updated_at":"2026-07-24T05:48:27Z","subjects":["TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Abu Hassan, Rozi Roslinda"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-01"]},{"key":"dc:date.issued","label":"Date","values":["2015-01"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Faculty of Electrical and Electronic Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Universiti Tun Hussein Onn Malaysia"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["http://eprints.uthm.edu.my/1448/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["mphil"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["TK5101-6720 Telecommunication. 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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."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["EEG signal classification for wheelchair control application"]}]}],"canonical_facts":{"dc:creator":["Abu Hassan, Rozi Roslinda"],"dc:date":["2015-01"],"dc:date.issued":["2015-01"],"dc:description.abstract":["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."],"dc:format":["text"],"dc:identifier.uri":["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"],"dc:language":["en"],"dc:publisher.department":["Faculty of Electrical and Electronic Engineering"],"dc:publisher.institution":["Universiti Tun Hussein Onn Malaysia"],"dc:relation.isreferencedby":["http://eprints.uthm.edu.my/1448/"],"dc:subject":["TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television"],"dc:title":["EEG signal classification for wheelchair control application"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["masters"],"dc:type.qualificationname":["mphil"]},"updated_at":"2026-07-24T05:48:27Z"}