Human Emotions Analysis and Recognition Using EEG Signals in Response to 360° Videos.

Emotion recognition (ER) technology is integral for developing innovative applications such as drowsiness detection and health monitoring that play a pivotal role in contemporary society. This study delves into ER using electroencephalography (EEG) within immersive virtual reality (VR) environments. Our proposed methodology has four main stages: 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. 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.