Graph-theoretical analysis of EEG-based functional connectivity during emotional experience in virtual reality for...

Virtual reality (VR) technologies can induce realistic emotions in controlled experimental settings, offering unprecedented opportunities to study how the human brain processes emotions under real-world conditions. The integration of VR experiences with electroencephalography (EEG) provides a promising potential for gaining novel insights into individual emotional states. However, the complex network dynamics underlying human emotions during VR experiences remain largely unexplored. To address this gap, we leveraged graph-theoretical approaches to investigate functional brain networks derived from EEG signals recorded during immersive VR experiments. We assessed key topological properties of functional brain networks across multiple frequency bands (delta, theta, alpha, beta, gamma, and high gamma) and compared network characteristics between different emotional states (negative, neutral, and positive). Furthermore, we evaluated whether these graph-based features could accurately distinguish between positive and negative emotions using machine learning approaches. Our findings revealed distinct network patterns associated with different emotional states. During negative emotional experiences, we observed two key neural signatures: increased high gamma band activity in the left central region and decreased theta band activity in the occipital region. Conversely, positive emotions were characterized by reduced activity across most frequency bands in the left frontal region. Our machine learning model achieved an average classification accuracy of 79% in differentiating positive and negative emotions using network features that combined graph-theoretical measures and connectivity weights across all frequency bands, with the high gamma band demonstrating particular importance for emotion processing. This study advances our understanding of how brain networks dynamically reorganize during VR-induced emotional experiences and establishes the potential of graph-based EEG features for robust emotion recognition, paving the way for personalized VR applications.