Title :
Classification of frontal alpha asymmetry using k-Nearest Neighbor
Author :
Aris, Siti Armiza Mohd ; Taib, Mohd Nasir ; Sulaiman, Norizam
Author_Institution :
UTM Razak Sch. of Eng. & Adv. Technol., Univ. Teknol. Malaysia, Kuala Lumpur, Malaysia
Abstract :
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.
Keywords :
brain; electroencephalography; feature extraction; fuzzy systems; medical signal processing; neurophysiology; pattern clustering; signal classification; EEG data collections; EEG feature; alpha band spectral power; asymmetry score; data segmentations; frontal alpha asymmetry; fuzzy C-means; k-nearest neighbor classifier; left brain; nonrelax condition; relax condition; right brain; signal classification; subtractive clustering; Brain modeling; Electroencephalography; Electrostatic discharges; Feature extraction; Psychology; Stress; EEG; Frontal Alpha Asymmetry; Fuzzy C-Means; Subtractive Clustering; k-NN;
Conference_Titel :
Biomedical Engineering (ICoBE), 2012 International Conference on
Conference_Location :
Penang
Print_ISBN :
978-1-4577-1990-5
DOI :
10.1109/ICoBE.2012.6178958
