Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals
Article
Uyanık, Hakan, Ozcelik, Salih Taha A., Duranay, Zeynep Bala Duranay, Sengur, Abdulkadir and Acharya, U. Rajendra. 2022. "Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals." Diagnostics. 12 (10). https://doi.org/10.3390/diagnostics12102508
| Article Title | Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals |
|---|---|
| ERA Journal ID | 212275 |
| Article Category | Article |
| Authors | Uyanık, Hakan, Ozcelik, Salih Taha A., Duranay, Zeynep Bala Duranay, Sengur, Abdulkadir and Acharya, U. Rajendra |
| Journal Title | Diagnostics |
| Journal Citation | 12 (10) |
| Article Number | 2508 |
| Number of Pages | 14 |
| Year | 2022 |
| Publisher | MDPI AG |
| Place of Publication | Switzerland |
| ISSN | 2075-4418 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/diagnostics12102508 |
| Web Address (URL) | https://www.mdpi.com/2075-4418/12/10/2508 |
| Abstract | Emotion recognition is one of the most important issues in human–computer interaction (HCI), neuroscience, and psychology fields. It is generally accepted that emotion recognition with neural data such as electroencephalography (EEG) signals, functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS) is better than other emotion detection methods such as speech, mimics, body language, facial expressions, etc., in terms of reliability and accuracy. In particular, EEG signals are bioelectrical signals that are frequently used because of the many advantages they offer in the field of emotion recognition. This study proposes an improved approach for EEG-based emotion recognition on a publicly available newly published dataset, VREED. Differential entropy (DE) features were extracted from four wavebands (theta 4–8 Hz, alpha 8–13 Hz, beta 13–30 Hz, and gamma 30–49 Hz) to classify two emotional states (positive/negative). Five classifiers, namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naïve Bayesian (NB), Decision Tree (DT), and Logistic Regression (LR) were employed with DE features for the automated classification of two emotional states. In this work, we obtained the best average accuracy of 76.22% ± 2.06 with the SVM classifier in the classification of two states. Moreover, we observed from the results that the highest average accuracy score was produced with the gamma band, as previously reported in studies in EEG-based emotion recognition. |
| Keywords | differential entropy; EEG signal; virtual reality (VR)-based emotions; SVM |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Export Date: 9 October 2023 |
| Byline Affiliations | Munzur University, Turkey |
| Bingol University, Turkey | |
| Firat University, Turkey | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
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https://research.usq.edu.au/item/z1w42/use-of-differential-entropy-for-automated-emotion-recognition-in-a-virtual-reality-environment-with-eeg-signals
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