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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Bibliographic Details
Published in:Diagnostics (Basel) 2022-10, Vol.12 (10), p.2508
Main Authors: Uyanık, Hakan, Ozcelik, Salih Taha A, Duranay, Zeynep Bala, Sengur, Abdulkadir, Acharya, U. Rajendra
Format: Article
Language:English
Subjects:
Accuracy
Asymmetry
Classification
Datasets
Deep learning
differential entropy
EEG signal
Electrocardiography
Electroencephalography
Emotions
Integrated software
Neural networks
Support vector machines
SVM
Virtual reality
virtual reality (VR)-based emotions
Wavelet transforms
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Summary: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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics12102508