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dc.contributor.authorSiti Armiza, Mohd Aris
dc.contributor.authorMohd Nasir, Taib
dc.contributor.authorNorizam, Sulaiman
dc.date.accessioned2012-08-16T03:58:26Z
dc.date.available2012-08-16T03:58:26Z
dc.date.issued2012-02-27
dc.identifier.citationp. 74-78en_US
dc.identifier.isbn978-145771989-9
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6178958
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/20721
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.org/en_US
dc.description.abstractFrontal 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.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofseriesProceedings of the International Conference on Biomedical Engineering (ICoBE 2012)en_US
dc.subjectElectroencephalogram (EEG)en_US
dc.subjectFrontal alpha asymmetryen_US
dc.subjectSubtractive clusteringen_US
dc.subjectFuzzy C-Means (FCM)en_US
dc.subjectk-Nearest Neighbor (k-NN)en_US
dc.titleClassification of frontal alpha asymmetry using k-Nearest neighboren_US
dc.typeWorking Paperen_US
dc.contributor.urlarmiza@ic.utm.myen_US
dc.contributor.urldr.nasir@ieee.orgen_US


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