EEG signal analysis and characterization for the aid of disabled people

EEG signal analysis and characterization for the aid of disabled people

The effectiveness of assistive devices for disabled people is often limited by the human machine interface. This research proposes an intelligent wheelchair system especially for severely disabled people based on analysing electroencephalographic signals by using discrete wavelet transform and hi...

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Main Authors: Reaz, Mamun Bin Ibne, Chowdhury, Md. Sazzad Hossien, Ibrahimy, Muhammad Ibn, Mohd-Yasin, Faisal
Format: Article
Language:English
Published: WIT Press 2007
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/29542/
http://irep.iium.edu.my/29542/
http://irep.iium.edu.my/29542/
http://irep.iium.edu.my/29542/1/EEG_signal_analysis_and_characterization_for.pdf
id iium-29542
recordtype eprints
spelling iium-295422013-03-28T02:39:13Z http://irep.iium.edu.my/29542/ EEG signal analysis and characterization for the aid of disabled people Reaz, Mamun Bin Ibne Chowdhury, Md. Sazzad Hossien Ibrahimy, Muhammad Ibn Mohd-Yasin, Faisal T Technology (General) The effectiveness of assistive devices for disabled people is often limited by the human machine interface. This research proposes an intelligent wheelchair system especially for severely disabled people based on analysing electroencephalographic signals by using discrete wavelet transform and higher order statistical methods. The system to be implemented in Field Programmable Gate Array enables an accurate and efficient system of processing signals to control the wheelchair, which makes an attractive option in the hardware realization. WIT Press 2007 Article PeerReviewed application/pdf en http://irep.iium.edu.my/29542/1/EEG_signal_analysis_and_characterization_for.pdf Reaz, Mamun Bin Ibne and Chowdhury, Md. Sazzad Hossien and Ibrahimy, Muhammad Ibn and Mohd-Yasin, Faisal (2007) EEG signal analysis and characterization for the aid of disabled people. WIT Transactions on Biomedicine and Health, 12. pp. 287-294. ISSN 1743-3525 http://www.witpress.com doi:10.2495/BIO070271
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)
Reaz, Mamun Bin Ibne
Chowdhury, Md. Sazzad Hossien
Ibrahimy, Muhammad Ibn
Mohd-Yasin, Faisal
EEG signal analysis and characterization for the aid of disabled people
description The effectiveness of assistive devices for disabled people is often limited by the human machine interface. This research proposes an intelligent wheelchair system especially for severely disabled people based on analysing electroencephalographic signals by using discrete wavelet transform and higher order statistical methods. The system to be implemented in Field Programmable Gate Array enables an accurate and efficient system of processing signals to control the wheelchair, which makes an attractive option in the hardware realization.
format Article
author Reaz, Mamun Bin Ibne
Chowdhury, Md. Sazzad Hossien
Ibrahimy, Muhammad Ibn
Mohd-Yasin, Faisal
author_facet Reaz, Mamun Bin Ibne
Chowdhury, Md. Sazzad Hossien
Ibrahimy, Muhammad Ibn
Mohd-Yasin, Faisal
author_sort Reaz, Mamun Bin Ibne
title EEG signal analysis and characterization for the aid of disabled people
title_short EEG signal analysis and characterization for the aid of disabled people
title_full EEG signal analysis and characterization for the aid of disabled people
title_fullStr EEG signal analysis and characterization for the aid of disabled people
title_full_unstemmed EEG signal analysis and characterization for the aid of disabled people
title_sort eeg signal analysis and characterization for the aid of disabled people
publisher WIT Press
publishDate 2007
url http://irep.iium.edu.my/29542/
http://irep.iium.edu.my/29542/
http://irep.iium.edu.my/29542/
http://irep.iium.edu.my/29542/1/EEG_signal_analysis_and_characterization_for.pdf
first_indexed 2023-09-18T20:43:23Z
last_indexed 2023-09-18T20:43:23Z
_version_ 1777409514220290048

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