Automated accurate emotion classification using Clefia pattern-based features with EEG signals
Article
Article Title | Automated accurate emotion classification using Clefia pattern-based features with EEG signals |
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ERA Journal ID | 40152 |
Article Category | Article |
Authors | Dogan, Abdullah, Barua, Prabal Datta, Baygin, Mehmet, Tuncer, Turker, Dogan, Sengul, Yaman, Orhan, Dogru, Ali Hikmet and Acharya, Rajendra U. |
Journal Title | International Journal of Healthcare Management |
Journal Citation | 17 (1), pp. 32-45 |
Number of Pages | 14 |
Year | 2024 |
Publisher | Taylor & Francis |
Place of Publication | United Kingdom |
ISSN | 1753-3031 |
1753-304X | |
2047-9700 | |
2047-9719 | |
Digital Object Identifier (DOI) | https://doi.org/10.1080/20479700.2022.2141694 |
Web Address (URL) | https://www.tandfonline.com/doi/full/10.1080/20479700.2022.2141694 |
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. |
Keywords | Clefia pattern; emotion classification; TQWT; mRMR; majority voting |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400399. Biomedical engineering not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Middle East Technical University, Turkey |
School of Business | |
University of Technology Sydney | |
Ardahan University, Turkiye | |
Firat University, Turkey | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
https://research.usq.edu.au/item/z01xy/automated-accurate-emotion-classification-using-clefia-pattern-based-features-with-eeg-signals
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