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

Abstract

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.

EEG-Based brain-machine interface for categorizing cognitive sentimental emotions

“…The authors indicated that one benefit of using EEG signals in related studies is their great temporal resolution, which can reach the millisecond level. Additionally, more recent EEG collection devices have received favorable assessments for clinical and real-world BMI use in terms of signal dependability and mobility (Jung et al 2022). In addition to assisting in the diagnosis of brain tumors from electroencephalography (EEG), the study of brain waves also aided in the treatment of intellectual deficiencies, including Alzheimer's disease, sleep disorders, and pilot tiredness.…”

Affective States and Virtual Reality to Improve Gait Rehabilitation: A Preliminary Study

“…Seizing these tools and creating personalized experiences either with a headset or on a screen can improve the user’s engagement, aiding his/her in achieving more challenging targets [ 25 ]. Multiple techniques range from machine learning [ 26 ] to multimodal frameworks [ 24 ] to classify the emotional states based on EEG and other sensors. This study’s objective was to analyze patients’ affective states during RAGT to determine under which conditions the measurement is accurate and can be used to create a dynamic feedback-based VR environment to improve the efficiency of the sessions.…”