Affective state classification through CMAC-based model of affects (CCMA) using SVM

Yaacob, Hamwira Sakti and Abdul Rahman, Abdul Wahab (2017) Affective state classification through CMAC-based model of affects (CCMA) using SVM. Advanced Science Letters, 23 (11). pp. 11369-11373. ISSN 1936-6612 E-ISSN 1936-7317 (In Press)

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Abstract

A number of computational models have been proposed to perform emotion profiling through affective state classification using EEG signals. However, such models do not include both temporal and spatial dynamic of the signals. It is also observed that the performance of classifying emotion using the existing models produce high classification accuracy on one subject, but not on different subjects. Thus, in this paper CMAC-based Computational Model of Affects (CCMA) is proposed as feature extraction for the classification task. CCMA keeps the temporal and spatial dynamics of EEG signals to produce better classification performance. Using Support Vector Machine (SVM) as classifier, the features produce higher classification accuracy for heterogeneous test.

Item Type:	Article (Journal)
Additional Information:	4870/60742
Uncontrolled Keywords:	CCMA; EEG; affective computing; SVM.
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Hamwira Yaacob
Date Deposited:	22 Jan 2018 10:39
Last Modified:	02 Apr 2018 11:16
URI:	http://irep.iium.edu.my/id/eprint/60742

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