EEG affect analysis based on KDE and MFCC

Classifying emotions based on the affective states of valence and arousal captured from brain discharge remains a challenge. The selection of the most efficient and reliable method of feature extraction forms a very important problem of EEG signal classification. Different methods applied are usuall...

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Main Authors:	Hamal, Abdul Qayoom, Othman, Marini, Yaacob, Hamwira Sakti, Abdul Rahman, Abdul Wahab
Format:	Conference or Workshop Item
Language:	English
Published:	2012
Subjects:	BF511 Affection. Feeling. Emotion T58.5 Information technology
Online Access:	http://irep.iium.edu.my/32132/ http://irep.iium.edu.my/32132/ http://irep.iium.edu.my/32132/1/32132.pdf

id	iium-32132
recordtype	eprints
spelling	iium-321322016-05-12T00:26:17Z http://irep.iium.edu.my/32132/ EEG affect analysis based on KDE and MFCC Hamal, Abdul Qayoom Othman, Marini Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab BF511 Affection. Feeling. Emotion T58.5 Information technology Classifying emotions based on the affective states of valence and arousal captured from brain discharge remains a challenge. The selection of the most efficient and reliable method of feature extraction forms a very important problem of EEG signal classification. Different methods applied are usually based upon the time-domain or frequency-domain analysis. The following study is devoted to the EEG affect analysis based on feature extraction using KDE and MFCC and comparison of results achieved. MLP is used for classification of the features extracted. The resultant feature vectors extracted using KDE provides a more accurate capture of basic emotions when compared with MFCC feature vectors. 2012 Conference or Workshop Item NonPeerReviewed application/pdf en http://irep.iium.edu.my/32132/1/32132.pdf Hamal, Abdul Qayoom and Othman, Marini and Yaacob, Hamwira Sakti and Abdul Rahman, Abdul Wahab (2012) EEG affect analysis based on KDE and MFCC. In: The ISCA 2nd International Conference on Advanced Computing and Communication (ISCA-ACC-2012), 27–29 June 2012, Los Angeles, California USA. http://www.isca-hq.org/ACC2012Program.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	BF511 Affection. Feeling. Emotion T58.5 Information technology
spellingShingle	BF511 Affection. Feeling. Emotion T58.5 Information technology Hamal, Abdul Qayoom Othman, Marini Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab EEG affect analysis based on KDE and MFCC
description	Classifying emotions based on the affective states of valence and arousal captured from brain discharge remains a challenge. The selection of the most efficient and reliable method of feature extraction forms a very important problem of EEG signal classification. Different methods applied are usually based upon the time-domain or frequency-domain analysis. The following study is devoted to the EEG affect analysis based on feature extraction using KDE and MFCC and comparison of results achieved. MLP is used for classification of the features extracted. The resultant feature vectors extracted using KDE provides a more accurate capture of basic emotions when compared with MFCC feature vectors.
format	Conference or Workshop Item
author	Hamal, Abdul Qayoom Othman, Marini Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab
author_facet	Hamal, Abdul Qayoom Othman, Marini Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab
author_sort	Hamal, Abdul Qayoom
title	EEG affect analysis based on KDE and MFCC
title_short	EEG affect analysis based on KDE and MFCC
title_full	EEG affect analysis based on KDE and MFCC
title_fullStr	EEG affect analysis based on KDE and MFCC
title_full_unstemmed	EEG affect analysis based on KDE and MFCC
title_sort	eeg affect analysis based on kde and mfcc
publishDate	2012
url	http://irep.iium.edu.my/32132/ http://irep.iium.edu.my/32132/ http://irep.iium.edu.my/32132/1/32132.pdf
first_indexed	2023-09-18T20:46:22Z
last_indexed	2023-09-18T20:46:22Z
_version_	1777409701974114304

EEG affect analysis based on KDE and MFCC

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