Universiti Teknologi Malaysia Institutional Repository

Classification of frontal alpha asymmetry using k-Nearest Neighbor

Mohd. Aris, Siti Armiza and Taib, Mohd. Nasir and Sulaiman, Norizam (2012) Classification of frontal alpha asymmetry using k-Nearest Neighbor. In: 2012 International Conference on Biomedical Engineering, ICoBE 2012. IEEE, New York, USA, pp. 74-78. ISBN 978-836295443-8

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Official URL: http://dx.doi.org/10.1109/ICoBE.2012.6178958


Frontal alpha asymmetry is used as the EEG feature in this study. Total number of 43 students participated in EEG data collections of relax and non-relax conditions. The spectral power of the alpha band for both left and right brain are extracted using data segmentations and then the Asymmetry Score (AS) is computed. Subtractive clustering is used to predetermine the number of cluster center that are presented in the data. While Fuzzy C-Means (FCM), is used to discriminate the EEG data into an appropriate cluster after the total number of cluster had been determined. The classification rate obtained from the k-Nearest Neighbor (k-NN) classifier is 84.62% which gives the highest classification rate.

Item Type:Book Section
Additional Information:Indexed by Scopus
Uncontrolled Keywords:EEG, frontalalphaasymmetry, fuzzyc-means, k-NN, subtractive clustering
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Razak School of Engineering and Advanced Technology
ID Code:34557
Deposited On:09 Oct 2013 06:44
Last Modified:04 Feb 2017 05:50

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