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Dysphoria detection using EEG signals

Dysphoria is a state faced when one experienced disappointment. If it is not handled properly, dysphoria may trigger acute stress, anxiety and depression. Typically, the individual who experienced dysphoria are in-denial because dysphoria is always being associated with negative connotations such...

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Main Authors: Kamaruddin, Norhaslinda, Mohd Nasir, Mohd Hafiz, Abdul Rahman, Abdul Wahab
Format: Article
Language:English
English
Published: ASTES Publishers 2019
Subjects:
Q Science (General)
QM Human anatomy
Online Access:http://irep.iium.edu.my/80674/7/80674%20Dysphoria%20Detection%20using%20EEG%20Signals.pdf
http://irep.iium.edu.my/80674/8/80674%20Dysphoria%20Detection%20using%20EEG%20Signals%20SCOPUS.pdf
http://irep.iium.edu.my/80674/
https://www.astesj.com/publications/ASTESJ_040424.pdf
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spelling my.iium.irep.806742020-06-12T02:56:56Z http://irep.iium.edu.my/80674/ Dysphoria detection using EEG signals Kamaruddin, Norhaslinda Mohd Nasir, Mohd Hafiz Abdul Rahman, Abdul Wahab Q Science (General) QM Human anatomy Dysphoria is a state faced when one experienced disappointment. If it is not handled properly, dysphoria may trigger acute stress, anxiety and depression. Typically, the individual who experienced dysphoria are in-denial because dysphoria is always being associated with negative connotations such as incompetency to handle pressure, weak personality and lack of will power. To date, there is no accurate instrument to measure dysphoria except using questionnaire by psychologists, such as: Depression, Anxiety and Stress Scale (DASS) and Nepean Dysphoria Scale (NDS-24). Participants may suppress or exaggerate their answers resulting in misdiagnosis. In this work, a theoretical Dysphoria Model of Affect (DMoA) is developed for dysphoria detection. Based on the hypothesis that dysphoria is related to negative emotion, the input from brain signal is captured using electroencephalogram (EEG) device to detect negative emotions. The results from analyzing the EEG signals were compared with DASS and NDS questionnaires for correlation analysis. It is observed that the proposed DMoA approach can identify negative emotions ranging from 55% to 77% accuracy. In addition, the NDS questionnaire seems to provide better distinction for dysphoria as compared to DASS and is similar to the result yielded by DMoA in detecting dysphoria. Thus, DMoA approach can be used as an alternative for early dysphoria detection to assist early intervention in identifying the patients’ mental states. Subsequently, DMoA approach can be implemented as another possible solution for early detection of dysphoria thus providing an enhancement to the present NDS instruments providing psychologists and psychiatrists with a quantitative tool for better analysis of the patients’ state. ASTES Publishers 2019-07-30 Article PeerReviewed application/pdf en http://irep.iium.edu.my/80674/7/80674%20Dysphoria%20Detection%20using%20EEG%20Signals.pdf application/pdf en http://irep.iium.edu.my/80674/8/80674%20Dysphoria%20Detection%20using%20EEG%20Signals%20SCOPUS.pdf Kamaruddin, Norhaslinda and Mohd Nasir, Mohd Hafiz and Abdul Rahman, Abdul Wahab (2019) Dysphoria detection using EEG signals. Advances in Science, Technology and Engineering Systems Journal, 4 (4). pp. 197-205. ISSN 2415-6698 https://www.astesj.com/publications/ASTESJ_040424.pdf 10.25046/aj040424
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic Q Science (General)
QM Human anatomy
spellingShingle Q Science (General)
QM Human anatomy
Kamaruddin, Norhaslinda
Mohd Nasir, Mohd Hafiz
Abdul Rahman, Abdul Wahab
Dysphoria detection using EEG signals
description Dysphoria is a state faced when one experienced disappointment. If it is not handled properly, dysphoria may trigger acute stress, anxiety and depression. Typically, the individual who experienced dysphoria are in-denial because dysphoria is always being associated with negative connotations such as incompetency to handle pressure, weak personality and lack of will power. To date, there is no accurate instrument to measure dysphoria except using questionnaire by psychologists, such as: Depression, Anxiety and Stress Scale (DASS) and Nepean Dysphoria Scale (NDS-24). Participants may suppress or exaggerate their answers resulting in misdiagnosis. In this work, a theoretical Dysphoria Model of Affect (DMoA) is developed for dysphoria detection. Based on the hypothesis that dysphoria is related to negative emotion, the input from brain signal is captured using electroencephalogram (EEG) device to detect negative emotions. The results from analyzing the EEG signals were compared with DASS and NDS questionnaires for correlation analysis. It is observed that the proposed DMoA approach can identify negative emotions ranging from 55% to 77% accuracy. In addition, the NDS questionnaire seems to provide better distinction for dysphoria as compared to DASS and is similar to the result yielded by DMoA in detecting dysphoria. Thus, DMoA approach can be used as an alternative for early dysphoria detection to assist early intervention in identifying the patients’ mental states. Subsequently, DMoA approach can be implemented as another possible solution for early detection of dysphoria thus providing an enhancement to the present NDS instruments providing psychologists and psychiatrists with a quantitative tool for better analysis of the patients’ state.
format Article
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 Dysphoria detection using EEG signals
title_short Dysphoria detection using EEG signals
title_full Dysphoria detection using EEG signals
title_fullStr Dysphoria detection using EEG signals
title_full_unstemmed Dysphoria detection using EEG signals
title_sort dysphoria detection using eeg signals
publisher ASTES Publishers
publishDate 2019
url http://irep.iium.edu.my/80674/7/80674%20Dysphoria%20Detection%20using%20EEG%20Signals.pdf
http://irep.iium.edu.my/80674/8/80674%20Dysphoria%20Detection%20using%20EEG%20Signals%20SCOPUS.pdf
http://irep.iium.edu.my/80674/
https://www.astesj.com/publications/ASTESJ_040424.pdf
_version_ 1672610197968781312
score 13.211869

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