Automated accurate emotion classification using Clefia pattern-based features with EEG signals

Dogan, A; Barua, PD; Baygin, M; Tuncer, T; Dogan, S; Yaman, O; Dogru, AH; Acharya, RU

Automated accurate emotion classification using Clefia pattern-based features with EEG signals

Dogan, A Barua, PD Baygin, M Tuncer, T Dogan, S Yaman, O Dogru, AH Acharya, RU

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Publisher:: Taylor and Francis Group
Publication Type:: Journal Article
Citation:: International Journal of Healthcare Management, 2024, 17, (1), pp. 32-45
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Dogan, A
dc.contributor.author	Barua, PD
dc.contributor.author	Baygin, M
dc.contributor.author	Tuncer, T
dc.contributor.author	Dogan, S
dc.contributor.author	Yaman, O
dc.contributor.author	Dogru, AH
dc.contributor.author	Acharya, RU
dc.date.accessioned	2025-01-28T02:39:04Z
dc.date.available	2025-01-28T02:39:04Z
dc.date.issued	2024-01-01
dc.identifier.citation	International Journal of Healthcare Management, 2024, 17, (1), pp. 32-45
dc.identifier.issn	2047-9719
dc.identifier.issn	2047-9719
dc.identifier.uri	http://hdl.handle.net/10453/184267
dc.description.abstract	Background: The electroencephalogram (EEG) emotion classification/recognition is one of the popular issues for advanced signal classification. However, it is difficult to manually screen the EEG signals as they are highly nonlinear and non-stationary. Methods: This paper introduces a novel nonlinear and multileveled features-based automatic EEG emotion classification method. Our presented EEG classification model uses feature vector creation deploying an S-Box-based local pattern with a decomposition (tunable q-factor wavelet transform is utilized), the most significant features chosen, classification a shallow machine learning method, and hard majority voting. The novel side of this research is the presented feature extractor since a component of the Clefia cipher has been considered to create a local feature extractor. Results: We have obtained an accuracy of 100.0%, 98.02%, 99.33%, and for valence, arousal, and dominance cases using the DEAP database. Also, we achieved 99.69%, 98.98%, and 99.66% accuracies for valence, dominance, and arousal cases with the DREAMER database. Our proposed model is able to classify arousal, dominance, and valence cases with an accuracy of more than 98% using both databases. Conclusions: The results show that the clefia pattern can perform automatic emotion classification with low computational complexity and high accuracy.
dc.language	en
dc.publisher	Taylor and Francis Group
dc.relation.ispartof	International Journal of Healthcare Management
dc.relation.isbasedon	10.1080/20479700.2022.2141694
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	4203 Health services and systems
dc.subject.classification	4206 Public health
dc.title	Automated accurate emotion classification using Clefia pattern-based features with EEG signals
dc.type	Journal Article
utslib.citation.volume	17
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2025-01-28T02:39:03Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	1

Abstract:

Background: The electroencephalogram (EEG) emotion classification/recognition is one of the popular issues for advanced signal classification. However, it is difficult to manually screen the EEG signals as they are highly nonlinear and non-stationary. Methods: This paper introduces a novel nonlinear and multileveled features-based automatic EEG emotion classification method. Our presented EEG classification model uses feature vector creation deploying an S-Box-based local pattern with a decomposition (tunable q-factor wavelet transform is utilized), the most significant features chosen, classification a shallow machine learning method, and hard majority voting. The novel side of this research is the presented feature extractor since a component of the Clefia cipher has been considered to create a local feature extractor. Results: We have obtained an accuracy of 100.0%, 98.02%, 99.33%, and for valence, arousal, and dominance cases using the DEAP database. Also, we achieved 99.69%, 98.98%, and 99.66% accuracies for valence, dominance, and arousal cases with the DREAMER database. Our proposed model is able to classify arousal, dominance, and valence cases with an accuracy of more than 98% using both databases. Conclusions: The results show that the clefia pattern can perform automatic emotion classification with low computational complexity and high accuracy.

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http://hdl.handle.net/10453/184267