Automated accurate emotion classification using Clefia pattern-based features with EEG signals
Article
Dogan, Abdullah, Barua, Prabal Datta, Baygin, Mehmet, Tuncer, Turker, Dogan, Sengul, Yaman, Orhan, Dogru, Ali Hikmet and Acharya, Rajendra U.. 2024. "Automated accurate emotion classification using Clefia pattern-based features with EEG signals." International Journal of Healthcare Management. 17 (1), pp. 32-45. https://doi.org/10.1080/20479700.2022.2141694
| Article Title | Automated accurate emotion classification using Clefia pattern-based features with EEG signals |
|---|---|
| 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 |
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