  Emotion recognition (ER) technology is an integral part for developing
innovative applications such as drowsiness detection and health monitoring that
plays a pivotal role in contemporary society. This study delves into ER using
electroencephalography (EEG), within immersive virtual reality (VR)
environments. There are four main stages in our proposed methodology including
data acquisition, pre-processing, feature extraction, and emotion
classification. Acknowledging the limitations of existing 2D datasets, we
introduce a groundbreaking 3D VR dataset to elevate the precision of emotion
elicitation. Leveraging the Interaxon Muse headband for EEG recording and
Oculus Quest 2 for VR stimuli, we meticulously recorded data from 40
participants, prioritizing subjects without reported mental illnesses.
Pre-processing entails rigorous cleaning, uniform truncation, and the
application of a Savitzky-Golay filter to the EEG data. Feature extraction
encompasses a comprehensive analysis of metrics such as power spectral density,
correlation, rational and divisional asymmetry, and power spectrum. To ensure
the robustness of our model, we employed a 10-fold cross-validation, revealing
an average validation accuracy of 85.54\%, with a noteworthy maximum accuracy
of 90.20\% in the best fold. Subsequently, the trained model demonstrated a
commendable test accuracy of 82.03\%, promising favorable outcomes.
