EEG emotion recognition based on the dimensional models of emotions

In this paper, we propose a method for EEG emotion recognition which is tested based on 2 dimensional models of emotions, (1) the rSASM, and (2) the 12-PAC model. EEG data were collected from 5 preschoolers aged 5 years old while watching emotional faces from the Radboud Faces Database (RafD). Fea...

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Main Authors: Othman, Marini, Abdul Rahman, Abdul Wahab, Karim, Izzah, Dzulkifli, Mariam Adawiah, Taha, Imad
Format: Article
Language:English
Published: Elsevier 2013
Subjects:
Online Access:http://irep.iium.edu.my/34903/1/EEG_emotion_recognition_based_on_the_dimensional_models_of.pdf
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author Othman, Marini
Abdul Rahman, Abdul Wahab
Karim, Izzah
Dzulkifli, Mariam Adawiah
Taha, Imad
author_facet Othman, Marini
Abdul Rahman, Abdul Wahab
Karim, Izzah
Dzulkifli, Mariam Adawiah
Taha, Imad
author_sort Othman, Marini
collection IIUM
description In this paper, we propose a method for EEG emotion recognition which is tested based on 2 dimensional models of emotions, (1) the rSASM, and (2) the 12-PAC model. EEG data were collected from 5 preschoolers aged 5 years old while watching emotional faces from the Radboud Faces Database (RafD). Features were extracted using KSDE and MFCC and classified using MLP. Results show that EEG emotion recognition using the 12-PAC model gives the highest accuracy for both feature extraction methods. Results indicated that the accuracy of EEG emotion recognition is increased with the precision of the dimensional models.
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spelling oai:generic.eprints.org:349032014-01-30T00:42:36Z http://irep.iium.edu.my/34903/ EEG emotion recognition based on the dimensional models of emotions Othman, Marini Abdul Rahman, Abdul Wahab Karim, Izzah Dzulkifli, Mariam Adawiah Taha, Imad QA75 Electronic computers. Computer science In this paper, we propose a method for EEG emotion recognition which is tested based on 2 dimensional models of emotions, (1) the rSASM, and (2) the 12-PAC model. EEG data were collected from 5 preschoolers aged 5 years old while watching emotional faces from the Radboud Faces Database (RafD). Features were extracted using KSDE and MFCC and classified using MLP. Results show that EEG emotion recognition using the 12-PAC model gives the highest accuracy for both feature extraction methods. Results indicated that the accuracy of EEG emotion recognition is increased with the precision of the dimensional models. Elsevier 2013 Article PeerReviewed application/pdf en http://irep.iium.edu.my/34903/1/EEG_emotion_recognition_based_on_the_dimensional_models_of.pdf Othman, Marini and Abdul Rahman, Abdul Wahab and Karim, Izzah and Dzulkifli, Mariam Adawiah and Taha, Imad (2013) EEG emotion recognition based on the dimensional models of emotions. Procedia - Social and Behavioral Sciences, 97. pp. 30-37. ISSN 1877-0428 http://dx.doi.org/10.1016/j.sbspro.2013.10.201 doi:10.1016/j.sbspro.2013.10.201
spellingShingle QA75 Electronic computers. Computer science
Othman, Marini
Abdul Rahman, Abdul Wahab
Karim, Izzah
Dzulkifli, Mariam Adawiah
Taha, Imad
EEG emotion recognition based on the dimensional models of emotions
title EEG emotion recognition based on the dimensional models of emotions
title_full EEG emotion recognition based on the dimensional models of emotions
title_fullStr EEG emotion recognition based on the dimensional models of emotions
title_full_unstemmed EEG emotion recognition based on the dimensional models of emotions
title_short EEG emotion recognition based on the dimensional models of emotions
title_sort eeg emotion recognition based on the dimensional models of emotions
topic QA75 Electronic computers. Computer science
url http://irep.iium.edu.my/34903/1/EEG_emotion_recognition_based_on_the_dimensional_models_of.pdf
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