EEG-based emotion recognition in an immersive virtual reality environment: From local activity to brain network features

Yu, Minchang; Xiao, Shasha; Hua, Minlei; Wang, Hui; Chen, Xi; Tian, Feng; Li, Yingjie

doi:10.1016/j.bspc.2021.103349

Cited by 47 publications

(24 citation statements)

References 76 publications

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“…To maintain a relatively consistent percentage in each class and each set compared with the original data, hold-out cross-validation was used by splitting each participant’s samples into a training set and a test set. Table 2 compares the obtained results with another reported work [ 38 ].…”

Section: Discussionmentioning

confidence: 67%

“…The authors reported that combining the relative power characteristics in the theta, alpha, beta, and gamma bands produced the best average accuracy of 73.77%. As seen in Table 2 , the obtained average accuracy score of 76.22% is better than the method by Yu et al [ 38 ]. The extracted feature is the main difference between the two works.…”

Section: Discussionmentioning

confidence: 71%

“…We compared our proposed method’s accuracy with the work by Yu et al [ 38 ]. They provided a baseline for the effectiveness of the classification of negative and positive emotions using the new dataset.…”

Section: Discussionmentioning

confidence: 99%

“…This paper proposes an EEG-based emotion classification approach for a 3D VR-based dataset [ 38 ]. In the proposed approach, the input EEG signals were initially preprocessed with MATLAB toolbox EEGLAB [ 39 ] for noise and baseline removals.…”

Section: Introductionmentioning

confidence: 99%

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Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals

et al. 2022

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“…To check neural state variations in VR conditions, the measured EEG signals were analyzed by reaction from participants. Yu et al [ 13 ] organized research designs using VR emotion recognition tasks and EEG measurements. Three different categories of stimuli (positive, neutral, and negative) were included in the tasks to compare emotional responses in experimental conditions.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction

Jung

Choi

Kim

et al. 2022

IJERPH

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Classifying emotional states is critical for brain–computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.

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P‐2.28: The Influence of Animals on Relaxation in Virtual Reality Scene

Zhao

Wang

et al. 2023

Symp Digest of Tech Papers

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There has been extensive research on emotions based on images and videos, yet there remains ample opportunities for exploring emotions in virtual reality technology. The scarcity of public virtual reality scenes for emotional research has prompted researchers to create their own. This study utilized the International Emotional Picture Library and the Chinese Emotional Picture Library to examine the effect of novel scenes that integrate environment and animals on inducing relaxation. This proposed research idea presents a new frontier for scene customization in virtual reality technology for emotional study.

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EEG-based emotion recognition in an immersive virtual reality environment: From local activity to brain network features

Cited by 47 publications

References 76 publications

Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals

Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals

EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction

P‐2.28: The Influence of Animals on Relaxation in Virtual Reality Scene

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