Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN

Emotions are postulated to be generated at the brain. To capture the brain activities during emotional processing, several neuro-imaging techniques have been adopted, including electroencephalogram (EEG). In the existing studies, different techniques have been employed to extract features from EEG s...

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Main Authors:	Yaacob, Hamwira Sakti, Abdul Rahman, Abdul Wahab, Kamaruddin, Norhaslinda
Format:	Conference or Workshop Item
Language:	English English
Published:	International Society of Computers and Their Applications (ISCA) 2014
Subjects:	BF511 Affection. Feeling. Emotion T Technology (General)
Online Access:	http://irep.iium.edu.my/43492/ http://irep.iium.edu.my/43492/ http://irep.iium.edu.my/43492/1/43492_Subject-dependen_complete.pdf http://irep.iium.edu.my/43492/2/43492_Subject-dependen_scopus.pdf

id	iium-43492
recordtype	eprints
spelling	iium-434922017-09-26T04:00:07Z http://irep.iium.edu.my/43492/ Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda BF511 Affection. Feeling. Emotion T Technology (General) Emotions are postulated to be generated at the brain. To capture the brain activities during emotional processing, several neuro-imaging techniques have been adopted, including electroencephalogram (EEG). In the existing studies, different techniques have been employed to extract features from EEG signals for emotion classification. However, existing feature extraction techniques do not consider spatial and temporal neural-dynamics of emotion. Furthermore, the non-linearity of EEG and self-adaptive of neural activations are disregard. Therefore, the classification accuracy of any feature extraction technique is inconsistent when applied with different classifiers. Hence, in this study, a new feature extraction technique that inculcates the qualities of EEG signal and the behavior neural activations based on Cerebellar Model Articulation Controller (CMAC) model is proposed. Classification performance of calm, fear, happiness and sadness using Evolving Fuzzy Neural Network (EFuNN) classifiers are compared based on subject-dependent and subject-independent validations. It is observed that the proposed technique is able to yield accuracy of above 50% to above 90% for subject-dependent classification. For subject-independent approach, the highest accuracy is barely 40%. The results suggest that this approach is comparable as a feature extraction technique for classifying emotions. International Society of Computers and Their Applications (ISCA) 2014-10 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/43492/1/43492_Subject-dependen_complete.pdf application/pdf en http://irep.iium.edu.my/43492/2/43492_Subject-dependen_scopus.pdf Yaacob, Hamwira Sakti and Abdul Rahman, Abdul Wahab and Kamaruddin, Norhaslinda (2014) Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN. In: 27th International Conference on Computer Applications in Industry and Engineering, 13-15 October 2014, New Orleans, Louisiana, USA. http://toc.proceedings.com/24275webtoc.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	BF511 Affection. Feeling. Emotion T Technology (General)
spellingShingle	BF511 Affection. Feeling. Emotion T Technology (General) Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN
description	Emotions are postulated to be generated at the brain. To capture the brain activities during emotional processing, several neuro-imaging techniques have been adopted, including electroencephalogram (EEG). In the existing studies, different techniques have been employed to extract features from EEG signals for emotion classification. However, existing feature extraction techniques do not consider spatial and temporal neural-dynamics of emotion. Furthermore, the non-linearity of EEG and self-adaptive of neural activations are disregard. Therefore, the classification accuracy of any feature extraction technique is inconsistent when applied with different classifiers. Hence, in this study, a new feature extraction technique that inculcates the qualities of EEG signal and the behavior neural activations based on Cerebellar Model Articulation Controller (CMAC) model is proposed. Classification performance of calm, fear, happiness and sadness using Evolving Fuzzy Neural Network (EFuNN) classifiers are compared based on subject-dependent and subject-independent validations. It is observed that the proposed technique is able to yield accuracy of above 50% to above 90% for subject-dependent classification. For subject-independent approach, the highest accuracy is barely 40%. The results suggest that this approach is comparable as a feature extraction technique for classifying emotions.
format	Conference or Workshop Item
author	Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda
author_facet	Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda
author_sort	Yaacob, Hamwira Sakti
title	Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN
title_short	Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN
title_full	Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN
title_fullStr	Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN
title_full_unstemmed	Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN
title_sort	subject-dependent and subject-independent emotional classification of cmac-based features using efunn
publisher	International Society of Computers and Their Applications (ISCA)
publishDate	2014
url	http://irep.iium.edu.my/43492/ http://irep.iium.edu.my/43492/ http://irep.iium.edu.my/43492/1/43492_Subject-dependen_complete.pdf http://irep.iium.edu.my/43492/2/43492_Subject-dependen_scopus.pdf
first_indexed	2023-09-18T21:01:58Z
last_indexed	2023-09-18T21:01:58Z
_version_	1777410682957856768

Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN

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