GDL

Journal from gdlhub / 2019-11-15 10:56:48
Oleh : Yulianto Tejo Putranto, Tri Arief Sardjono, Mochamad Hariadi, Mauridhi Hery Purnomo, JNTETI
Dibuat : 2019-05-08, dengan 1 file

Keyword : Brain-Computer Interface, EEG motor imagery, differential asymmetry, SVM, k-NN, Tree
Url : http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/view/493
Sumber pengambilan dokumen : WEB

Brain-Computer Interface (BCI) technology has enabled people with motor disabilities to interact with their environment. The electroencephalograph (EEG) signals related to a motor imagery movement were used as a control signal. In this paper, EEG motor imagery signals from the 2-class data have been processed into features and classified. The power and standard deviation of EEG signals, mean of absolute wavelet coefficients, and the average power of the wavelet coefficients were used as features. The purpose of this paper is to apply the differential asymmetry of these features as new features to improve the system accuracy. As a classifier, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Tree were used. The result shows that for dataset I the use of differential asymmetry as feature can increase the system accuracy up to 47.8%, from 52.20% to 100%, with Tree as a classifier. For dataset II, it can increase accuracy by 8.46%, from 54.42% to 62.48%.

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