Extracting features using computational cerebellar model for emotion classification

Several feature extraction techniques have been employed to extract features from EEG signals for classifying emotions. Such techniques are not constructed based on the understanding of EEG and brain functions, neither inspired by the understanding of emotional dynamics. Hence, the features are diff...

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Main Authors:	Yaacob, Hamwira Sakti, Abdul Rahman, Abdul Wahab, Kamaruddin, Norhaslinda
Format:	Conference or Workshop Item
Language:	English English
Published:	2013
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/38073/ http://irep.iium.edu.my/38073/ http://irep.iium.edu.my/38073/1/Extracting_features_using_computational_cerebellar_model_for_emotion_classification.pdf http://irep.iium.edu.my/38073/4/38073_Extracting%20features%20using_Scopus.pdf

id	iium-38073
recordtype	eprints
spelling	iium-380732017-09-07T01:59:45Z http://irep.iium.edu.my/38073/ Extracting features using computational cerebellar model for emotion classification Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda TK7885 Computer engineering Several feature extraction techniques have been employed to extract features from EEG signals for classifying emotions. Such techniques are not constructed based on the understanding of EEG and brain functions, neither inspired by the understanding of emotional dynamics. Hence, the features are difficult to be interpreted and yield low classification performance. In this study, a new feature extraction technique using Cerebellar Model Articulation Controller (CMAC) is proposed. The features are extracted from the weights of datadriven self-organizing feature map that are adjusted during training to optimize the error obtained from the desired output and the calculated output. Multi-Layer Perceptron (MLP) classifier is then employed to perform classification on fear, happiness, sadness and calm emotions. Experimental results show that the average accuracy of classifying emotions from EEG signals captured on 12 children aged between 4 to 6 years old ranging from 84.18% to 89.29%. In addition, classification performance for features derived from other techniques such as Power Spectrum Density (PSD), Kernel Density Estimation (KDE) and Mel-Frequency Cepstral Coefficients (MFCC) are also presented as a standard benchmark for comparison purpose. It is observed that the proposed approach is able to yield accuracy of 33.77% to 55% as compared to the respective comparison features. The experimental results indicated that the proposed approach has potential for comparative emotion recognition accuracy when coupled with MLP. 2013 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/38073/1/Extracting_features_using_computational_cerebellar_model_for_emotion_classification.pdf application/pdf en http://irep.iium.edu.my/38073/4/38073_Extracting%20features%20using_Scopus.pdf Yaacob, Hamwira Sakti and Abdul Rahman, Abdul Wahab and Kamaruddin, Norhaslinda (2013) Extracting features using computational cerebellar model for emotion classification. In: 2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 23-24 Dec. 2013, Kuching, Sarawak. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6836608&tag=1
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda Extracting features using computational cerebellar model for emotion classification
description	Several feature extraction techniques have been employed to extract features from EEG signals for classifying emotions. Such techniques are not constructed based on the understanding of EEG and brain functions, neither inspired by the understanding of emotional dynamics. Hence, the features are difficult to be interpreted and yield low classification performance. In this study, a new feature extraction technique using Cerebellar Model Articulation Controller (CMAC) is proposed. The features are extracted from the weights of datadriven self-organizing feature map that are adjusted during training to optimize the error obtained from the desired output and the calculated output. Multi-Layer Perceptron (MLP) classifier is then employed to perform classification on fear, happiness, sadness and calm emotions. Experimental results show that the average accuracy of classifying emotions from EEG signals captured on 12 children aged between 4 to 6 years old ranging from 84.18% to 89.29%. In addition, classification performance for features derived from other techniques such as Power Spectrum Density (PSD), Kernel Density Estimation (KDE) and Mel-Frequency Cepstral Coefficients (MFCC) are also presented as a standard benchmark for comparison purpose. It is observed that the proposed approach is able to yield accuracy of 33.77% to 55% as compared to the respective comparison features. The experimental results indicated that the proposed approach has potential for comparative emotion recognition accuracy when coupled with MLP.
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	Extracting features using computational cerebellar model for emotion classification
title_short	Extracting features using computational cerebellar model for emotion classification
title_full	Extracting features using computational cerebellar model for emotion classification
title_fullStr	Extracting features using computational cerebellar model for emotion classification
title_full_unstemmed	Extracting features using computational cerebellar model for emotion classification
title_sort	extracting features using computational cerebellar model for emotion classification
publishDate	2013
url	http://irep.iium.edu.my/38073/ http://irep.iium.edu.my/38073/ http://irep.iium.edu.my/38073/1/Extracting_features_using_computational_cerebellar_model_for_emotion_classification.pdf http://irep.iium.edu.my/38073/4/38073_Extracting%20features%20using_Scopus.pdf
first_indexed	2023-09-18T20:54:38Z
last_indexed	2023-09-18T20:54:38Z
_version_	1777410222139113472

Extracting features using computational cerebellar model for emotion classification

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