EEG affective modelling for dysphoria understanding

Dysphoria is a state of dissatisfaction, restlessness or fidgeting. It is a state of feeling unwell in relation to mental and emotional discomfort. If this state is not carefully handled, it may lead to depression, anxiety, and stress. To date, 21-item instruments of Depression, Anxiety and Stress S...

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Main Authors:	Kamaruddin, Norhaslinda, Mohd Nasir, Mohd Hafiz, Abdul Rahman, Abdul Wahab
Format:	Conference or Workshop Item
Language:	English English
Published:	IEEE Computer Society 2018
Subjects:	RC Internal medicine
Online Access:	http://irep.iium.edu.my/70800/ http://irep.iium.edu.my/70800/ http://irep.iium.edu.my/70800/ http://irep.iium.edu.my/70800/1/70800_EEG%20affective%20modelling_SCOPUS.pdf http://irep.iium.edu.my/70800/7/70800_EEG%20affective%20modelling%20for%20dysphoria%20understanding.pdf

id	iium-70800
recordtype	eprints
spelling	iium-708002019-04-10T03:36:54Z http://irep.iium.edu.my/70800/ EEG affective modelling for dysphoria understanding Kamaruddin, Norhaslinda Mohd Nasir, Mohd Hafiz Abdul Rahman, Abdul Wahab RC Internal medicine Dysphoria is a state of dissatisfaction, restlessness or fidgeting. It is a state of feeling unwell in relation to mental and emotional discomfort. If this state is not carefully handled, it may lead to depression, anxiety, and stress. To date, 21-item instruments of Depression, Anxiety and Stress Scale (DASS) is employed to measure dysphoria. Although DASS provides a quantitative assessment of the human affective state, it is subjected to interpretation. To complicate matters, pre-cursor emotion and pre-emotion of the participants can result in biasness of the DASS report. Hence, a more direct method in measuring human affective state by analyzing the brain pattern is proposed. The approach can also address the dynamic affective state which is needed in detecting dysphoria. Brain waves pattern are collected using the electroencephalogram (EEG) device and used as the input to analyze the underlying emotion. In this paper, relevant features were extracted using Mel-frequency cepstral coefficients (MFCC) and classified with Multi-Layer Perceptron (MLP). The experimental results show potential of differentiating between positive and negative emotion with comparable accuracy. Subsequently, it is envisaged that the proposed model can be extended as a tool that can be used to measure stress and anxiety in work places and education institutions. IEEE Computer Society 2018 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/70800/1/70800_EEG%20affective%20modelling_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/70800/7/70800_EEG%20affective%20modelling%20for%20dysphoria%20understanding.pdf Kamaruddin, Norhaslinda and Mohd Nasir, Mohd Hafiz and Abdul Rahman, Abdul Wahab (2018) EEG affective modelling for dysphoria understanding. In: 12th International Symposium on Medical Information and Communication Technology, ISMICT 2018, 26th-28th March 2018, Sydney; Australia. https://ieeexplore.ieee.org/document/8573716 10.1109/ISMICT.2018.8573716
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	RC Internal medicine
spellingShingle	RC Internal medicine Kamaruddin, Norhaslinda Mohd Nasir, Mohd Hafiz Abdul Rahman, Abdul Wahab EEG affective modelling for dysphoria understanding
description	Dysphoria is a state of dissatisfaction, restlessness or fidgeting. It is a state of feeling unwell in relation to mental and emotional discomfort. If this state is not carefully handled, it may lead to depression, anxiety, and stress. To date, 21-item instruments of Depression, Anxiety and Stress Scale (DASS) is employed to measure dysphoria. Although DASS provides a quantitative assessment of the human affective state, it is subjected to interpretation. To complicate matters, pre-cursor emotion and pre-emotion of the participants can result in biasness of the DASS report. Hence, a more direct method in measuring human affective state by analyzing the brain pattern is proposed. The approach can also address the dynamic affective state which is needed in detecting dysphoria. Brain waves pattern are collected using the electroencephalogram (EEG) device and used as the input to analyze the underlying emotion. In this paper, relevant features were extracted using Mel-frequency cepstral coefficients (MFCC) and classified with Multi-Layer Perceptron (MLP). The experimental results show potential of differentiating between positive and negative emotion with comparable accuracy. Subsequently, it is envisaged that the proposed model can be extended as a tool that can be used to measure stress and anxiety in work places and education institutions.
format	Conference or Workshop Item
author	Kamaruddin, Norhaslinda Mohd Nasir, Mohd Hafiz Abdul Rahman, Abdul Wahab
author_facet	Kamaruddin, Norhaslinda Mohd Nasir, Mohd Hafiz Abdul Rahman, Abdul Wahab
author_sort	Kamaruddin, Norhaslinda
title	EEG affective modelling for dysphoria understanding
title_short	EEG affective modelling for dysphoria understanding
title_full	EEG affective modelling for dysphoria understanding
title_fullStr	EEG affective modelling for dysphoria understanding
title_full_unstemmed	EEG affective modelling for dysphoria understanding
title_sort	eeg affective modelling for dysphoria understanding
publisher	IEEE Computer Society
publishDate	2018
url	http://irep.iium.edu.my/70800/ http://irep.iium.edu.my/70800/ http://irep.iium.edu.my/70800/ http://irep.iium.edu.my/70800/1/70800_EEG%20affective%20modelling_SCOPUS.pdf http://irep.iium.edu.my/70800/7/70800_EEG%20affective%20modelling%20for%20dysphoria%20understanding.pdf
first_indexed	2023-09-18T21:40:30Z
last_indexed	2023-09-18T21:40:30Z
_version_	1777413107565461504

EEG affective modelling for dysphoria understanding

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