Affective computation on EEG correlates of emotion from musical and vocal stimuli

Khosrowabadi, Reza and Abdul Rahman, Abdul Wahab and Ang, Kai Keng and H Baniasad, Mohammad. (2009) Affective computation on EEG correlates of emotion from musical and vocal stimuli. In: The 2009 International Joint Conference on Neural Networks (IJCNN 2009), 14-19 June 2009, Atlanta, Georgia, USA.

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Abstract

Affective interface that acquires and detects the emotion of the user can potentially enhance the humancomputer interface experience. In this paper, an affective brain-computer interface (ABCI) is proposed to perform affective computation on electroencephalogram (EEG) correlates of emotion. The proposed ABCI extracts EEG features from subjects while exposed to 6 emotionally-related musical and vocal stimuli using kernel smoothing density estimation (KSDE) and Gaussian mixture model probability estimation (GMM). A classification algorithm is subsequently used to learn and classify the extracted EEG features. An intersubject validation study is performed on healthy subjects to assess the performance of ABCI using a selection of classification algorithms. The results show that ABCI that employed the Bayesian network and the One-Rule classifier yielded a promising inter-subject validation accuracy of 90%.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Additional Information:	6145/38139
Uncontrolled Keywords:	EEG
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology
Depositing User:	Prof Abdul Wahab Abdul Rahman
Date Deposited:	11 Sep 2014 12:10
Last Modified:	16 Dec 2020 23:53
URI:	http://irep.iium.edu.my/id/eprint/38139

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