Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals
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-in...
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MDPI AG
2022-10-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/10/2508 |
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| author | Hakan Uyanık Salih Taha A. Ozcelik Zeynep Bala Duranay Abdulkadir Sengur U. Rajendra Acharya |
| author_facet | Hakan Uyanık Salih Taha A. Ozcelik Zeynep Bala Duranay Abdulkadir Sengur U. Rajendra Acharya |
| author_sort | Hakan Uyanık |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T20:21:41Z |
| format | Article |
| id | doaj.art-c5985f9570ae4b429f0b082e5b2d9f2b |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T20:21:41Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-c5985f9570ae4b429f0b082e5b2d9f2b2023-11-23T23:46:31ZengMDPI AGDiagnostics2075-44182022-10-011210250810.3390/diagnostics12102508Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG SignalsHakan Uyanık0Salih Taha A. Ozcelik1Zeynep Bala Duranay2Abdulkadir Sengur3U. Rajendra Acharya4Electrical-Electronics Engineering Department, Engineering Faculty, Munzur University, Tunceli 62000, TurkeyElectrical-Electronics Engineering Department, Engineering Faculty, Bingol University, Bingol 12000, TurkeyElectrical-Electronics Engineering Department, Technology Faculty, Firat University, Elazig 23119, TurkeyElectrical-Electronics Engineering Department, Technology Faculty, Firat University, Elazig 23119, TurkeyDepartment of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, SingaporeEmotion 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.https://www.mdpi.com/2075-4418/12/10/2508EEG signalvirtual reality (VR)-based emotionsdifferential entropySVM |
| spellingShingle | Hakan Uyanık Salih Taha A. Ozcelik Zeynep Bala Duranay Abdulkadir Sengur U. Rajendra Acharya Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals Diagnostics EEG signal virtual reality (VR)-based emotions differential entropy SVM |
| title | Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals |
| title_full | Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals |
| title_fullStr | Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals |
| title_full_unstemmed | Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals |
| title_short | Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals |
| title_sort | use of differential entropy for automated emotion recognition in a virtual reality environment with eeg signals |
| topic | EEG signal virtual reality (VR)-based emotions differential entropy SVM |
| url | https://www.mdpi.com/2075-4418/12/10/2508 |
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