Abstract
Brain Computer Interface or BCI is a technology that creates new communication channel where human brain (via Electroencephalography) can communicate with electronic devices.EEG signal is produced by the neurons, where every thought, emotion and movement can generate different patterns of EEG signal. There are two objectives defined for this research. The first objective is to compare the EEG data generated for actual and imaginary motor movement when lifting the left and right hand by using Support Vector Machine (SVM).The second objective is to find the correlation in EEG pattern between the actual motor movement and imaginary motor movement data, which is also based on SVM classification analysis. From the classification analysis, the accuracy for actual left and right-hand lifting movement is obtained at 90%. Meanwhile, the accuracy for classifying EEG data of imaginary left and right-hand lifting movement is obtained at 75%. In finding the correlation between the actual and imaginary EEG data, a classification analysis is also done by combining the actual and imaginary data. In this experiment, the accuracy in classifying the left and right-hand lifting activities is obtained at 78.8%. The significant accuracy measures obtained means that there is some correlation in EEG patterns between the actual motor movement and imaginary motor movement of lifting either left or right hand.
Metadata
Item Type: | Article |
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Creators: | Creators Email / ID Num. Hamzah, Nabilah UNSPECIFIED Zaini, Norliza UNSPECIFIED Sani, Maizura UNSPECIFIED Ismail, Nurlaila UNSPECIFIED |
Subjects: | T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics > Computer engineering. Computer hardware |
Divisions: | Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering |
Journal or Publication Title: | Journal of Electrical and Electronic Systems Research (JEESR) |
UiTM Journal Collections: | UiTM Journal > Journal of Electrical and Electronic Systems Research (JEESR) |
ISSN: | 1985-5389 |
Volume: | 10 |
Keywords: | EEG, Power Spectral Density, Support Vector Machine |
Date: | June 2017 |
URI: | https://ir.uitm.edu.my/id/eprint/29587 |