eprintid: 4933 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/00/49/33 datestamp: 2023-11-09 16:16:38 lastmod: 2023-11-09 16:16:38 status_changed: 2023-11-09 15:59:57 type: conference_item metadata_visibility: show creators_name: Abdalsalam M., E. creators_name: Yusoff, M.Z. creators_name: Kamel, N. creators_name: Malik, A. creators_name: Meselhy, M. title: Mental task motor imagery classifications for noninvasive brain computer interface ispublished: pub keywords: Artificial intelligence; Discrete wavelet transforms; Electroencephalography; Feature extraction; Learning algorithms; Wavelet transforms, Alternative communication; Bagging; Control panels; Measurement device; MLP; Motor imagery classification; Motor response; Multi-layer perception, Brain computer interface note: cited By 12; Conference of 2014 5th International Conference on Intelligent and Advanced Systems, ICIAS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:107042 abstract: A Brain computer interface (BCI) has introduced new scope and created a new period for developers and researchers giving alternative communication channels for paralysed peoples. Motor imagery refers to where EEG signals that being obtained while the subject is imagining or performing a motor response. This work is to examine this area from Machine Learning and exploit the Emotiv System as a cost-effective, noninvasive and also a portable EEG measurement device. The experiment was carried out based on Emotiv control panel focusing on cognitive commands such as (forward, backward, left and right). The data were preprocessed to remove the artifact as well as the noise by using EEGlab toolbox. Wavelet transforms namely Daubechies and symlets were used for feature extraction. The Multilayer perception (MLP), Simple logistic and Bagging were utilized to classify the mental tasks motor imagery. The performance of classifications was tested and satisfactory results were obtained with the accuracy rate 80.4 using the Simple logistic classifier. © 2014 IEEE. date: 2014 publisher: IEEE Computer Society official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906342282&doi=10.1109%2fICIAS.2014.6869531&partnerID=40&md5=08a733231c57e3be1f7610019fc8c8e7 id_number: 10.1109/ICIAS.2014.6869531 full_text_status: none publication: 2014 5th International Conference on Intelligent and Advanced Systems: Technological Convergence for Sustainable Future, ICIAS 2014 - Proceedings place_of_pub: Kuala Lumpur refereed: TRUE isbn: 9781479946549 citation: Abdalsalam M., E. and Yusoff, M.Z. and Kamel, N. and Malik, A. and Meselhy, M. (2014) Mental task motor imagery classifications for noninvasive brain computer interface. In: UNSPECIFIED.