CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals

Several studies have been performed to profile emotions using EEG signals through affective computing approach. It includes data acquisition, signal pre-processing, feature extraction and classification. Different combinations of feature extraction and classification techniques have been propos...

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Main Authors:	Yaacob, Hamwira Sakti, Abdul Rahman, Abdul Wahab, Alshaikhli, Imad Fakhri Taha, Kamaruddin, Norhaslinda
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/40480/ http://irep.iium.edu.my/40480/ http://irep.iium.edu.my/40480/1/40480.pdf

id	iium-40480
recordtype	eprints
spelling	iium-404802018-05-24T05:50:36Z http://irep.iium.edu.my/40480/ CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Alshaikhli, Imad Fakhri Taha Kamaruddin, Norhaslinda QA75 Electronic computers. Computer science Several studies have been performed to profile emotions using EEG signals through affective computing approach. It includes data acquisition, signal pre-processing, feature extraction and classification. Different combinations of feature extraction and classification techniques have been proposed. However, the results are subjective. Very few studies include subject-independent classification. In this paper, a new profiling model, known as CMAC-based Computational Model of Affects (CCMA), is proposed. ), CMAC is presumed to be a reasonable model for processing EEG signals with its innate capabilities to solve non-linear problems through selforganization feature mapping (SOFM). Features that are extracted using CCMA are trained using Evolving Fuzzy Neural Network (EFuNN) as the classifier. For comparison, classification of emotions using features that are derived from power spectral density (PSD) was also performed. The results shows that the performance of using CCMA for profiling emotions outperforms the performance of classifying emotions from PSD features. 2014-11-17 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/40480/1/40480.pdf Yaacob, Hamwira Sakti and Abdul Rahman, Abdul Wahab and Alshaikhli, Imad Fakhri Taha and Kamaruddin, Norhaslinda (2014) CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals. In: 5th International Conference on Information and Communication Technology for The Muslim World (ICT4M 2014), 17th - 19th November 2014, Kuching, Sarawak, Malaysia. (Unpublished) http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7020584
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Alshaikhli, Imad Fakhri Taha Kamaruddin, Norhaslinda CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals
description	Several studies have been performed to profile emotions using EEG signals through affective computing approach. It includes data acquisition, signal pre-processing, feature extraction and classification. Different combinations of feature extraction and classification techniques have been proposed. However, the results are subjective. Very few studies include subject-independent classification. In this paper, a new profiling model, known as CMAC-based Computational Model of Affects (CCMA), is proposed. ), CMAC is presumed to be a reasonable model for processing EEG signals with its innate capabilities to solve non-linear problems through selforganization feature mapping (SOFM). Features that are extracted using CCMA are trained using Evolving Fuzzy Neural Network (EFuNN) as the classifier. For comparison, classification of emotions using features that are derived from power spectral density (PSD) was also performed. The results shows that the performance of using CCMA for profiling emotions outperforms the performance of classifying emotions from PSD features.
format	Conference or Workshop Item
author	Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Alshaikhli, Imad Fakhri Taha Kamaruddin, Norhaslinda
author_facet	Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Alshaikhli, Imad Fakhri Taha Kamaruddin, Norhaslinda
author_sort	Yaacob, Hamwira Sakti
title	CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals
title_short	CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals
title_full	CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals
title_fullStr	CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals
title_full_unstemmed	CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals
title_sort	cmac-based computational model of affects (ccma) for profiling emotion from eeg signals
publishDate	2014
url	http://irep.iium.edu.my/40480/ http://irep.iium.edu.my/40480/ http://irep.iium.edu.my/40480/1/40480.pdf
first_indexed	2023-09-18T20:58:05Z
last_indexed	2023-09-18T20:58:05Z
_version_	1777410439268794368

CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals

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