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

fYear

2012

Firstpage

74

Lastpage

78

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering (ICoBE), 2012 International Conference on

Conference_Location

Penang

Print_ISBN

978-1-4577-1990-5

Type

conf

DOI

10.1109/ICoBE.2012.6178958

Filename

6178958