Emotion recognition model using source-temporal features and fuzzy

This paper aims to evaluate the performance of Electroencephalographic (EEG) emotion recognition system (EEG-ER) using source-temporal domain with Takagi- Sugeno-Kang (TSK) fuzzy model. Ten healthy subjects aged 5-6 years were participated in this study. Emotion elicitation procedure has done usi...

Full description

Bibliographic Details
Main Authors:	Shams, Wafa Khazal, Abdul Rahman, Abdul Wahab, Taha Alshaikhli, Imad Fakhri
Format:	Article
Language:	English
Published:	Springer-Verlag, Berlin, Germany 2013
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/34356/ http://irep.iium.edu.my/34356/ http://irep.iium.edu.my/34356/1/Emotion_Recognition_Model_Using_Source-Temporal_Features_and_Fuzzy.pdf

id	iium-34356
recordtype	eprints
spelling	iium-343562018-05-24T07:43:59Z http://irep.iium.edu.my/34356/ Emotion recognition model using source-temporal features and fuzzy Shams, Wafa Khazal Abdul Rahman, Abdul Wahab Taha Alshaikhli, Imad Fakhri QA75 Electronic computers. Computer science This paper aims to evaluate the performance of Electroencephalographic (EEG) emotion recognition system (EEG-ER) using source-temporal domain with Takagi- Sugeno-Kang (TSK) fuzzy model. Ten healthy subjects aged 5-6 years were participated in this study. Emotion elicitation procedure has done using the Radbound faces database (RafD). The selected emotions were happy, sad, and neutral and fear. The results were compared with wavelet coefficients (WC) as feature extraction method and Regularized Least Square (RLS) and Multi-Layer Perception (MLP) neural network classifiers from our previous work. Another comparison was done between affective model of Russell and RafD. The results show the efficiency of using source-temporal features in emotion recognition system hence there was a slight difference in accuracy among different classifier; MLP, RLS and TSK however MLP and TSK results were with high accurate and stable. Moreover Russell model which is based on positive-negative dimensions shows high accuracy than RafD model that has positive dimensions. The accuracy was around 97% using Russell model. Springer-Verlag, Berlin, Germany 2013 Article PeerReviewed application/pdf en http://irep.iium.edu.my/34356/1/Emotion_Recognition_Model_Using_Source-Temporal_Features_and_Fuzzy.pdf Shams, Wafa Khazal and Abdul Rahman, Abdul Wahab and Taha Alshaikhli, Imad Fakhri (2013) Emotion recognition model using source-temporal features and fuzzy. Lecture Notes in Computer Science (LNCS), 8226 (1). pp. 577-584. ISSN 1611-3349 (O), 0302-9743 (P) http://www.springer.com/computer/lncs?SGWID=0-164-6-1068921-0
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 Shams, Wafa Khazal Abdul Rahman, Abdul Wahab Taha Alshaikhli, Imad Fakhri Emotion recognition model using source-temporal features and fuzzy
description	This paper aims to evaluate the performance of Electroencephalographic (EEG) emotion recognition system (EEG-ER) using source-temporal domain with Takagi- Sugeno-Kang (TSK) fuzzy model. Ten healthy subjects aged 5-6 years were participated in this study. Emotion elicitation procedure has done using the Radbound faces database (RafD). The selected emotions were happy, sad, and neutral and fear. The results were compared with wavelet coefficients (WC) as feature extraction method and Regularized Least Square (RLS) and Multi-Layer Perception (MLP) neural network classifiers from our previous work. Another comparison was done between affective model of Russell and RafD. The results show the efficiency of using source-temporal features in emotion recognition system hence there was a slight difference in accuracy among different classifier; MLP, RLS and TSK however MLP and TSK results were with high accurate and stable. Moreover Russell model which is based on positive-negative dimensions shows high accuracy than RafD model that has positive dimensions. The accuracy was around 97% using Russell model.
format	Article
author	Shams, Wafa Khazal Abdul Rahman, Abdul Wahab Taha Alshaikhli, Imad Fakhri
author_facet	Shams, Wafa Khazal Abdul Rahman, Abdul Wahab Taha Alshaikhli, Imad Fakhri
author_sort	Shams, Wafa Khazal
title	Emotion recognition model using source-temporal features and fuzzy
title_short	Emotion recognition model using source-temporal features and fuzzy
title_full	Emotion recognition model using source-temporal features and fuzzy
title_fullStr	Emotion recognition model using source-temporal features and fuzzy
title_full_unstemmed	Emotion recognition model using source-temporal features and fuzzy
title_sort	emotion recognition model using source-temporal features and fuzzy
publisher	Springer-Verlag, Berlin, Germany
publishDate	2013
url	http://irep.iium.edu.my/34356/ http://irep.iium.edu.my/34356/ http://irep.iium.edu.my/34356/1/Emotion_Recognition_Model_Using_Source-Temporal_Features_and_Fuzzy.pdf
first_indexed	2023-09-18T20:49:32Z
last_indexed	2023-09-18T20:49:32Z
_version_	1777409900922535936

Emotion recognition model using source-temporal features and fuzzy

Similar Items