The Responses of EEG High Gamma to Emotional Virtual Reality Stimuli: Insights from Local Activation and distributed characteristics

Shasha Xiao¹Nadia Youssef²Qingxun Zhang¹Xiaoqian Lin¹Ziquan Qiu¹Wenjie Liu¹Xianglian Meng¹Minchang Yu^1*

• ¹School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou, China
• ²Knowlepsy, Marseille, Provence-Alpes-Côte d'Azur, France

The current understanding of emotion-related neural mechanisms in virtual reality (VR) remains limited, particularly regarding local activations and distributed network characteristics during different emotional states. In this study, electroencephalogram (EEG) responses to positive and negative VR stimuli were analyzed by recording EEG data from 19 participants while they viewed 4-second VR videos that elicited these emotions. Two neural signatures were examined: high gamma band (53-80 Hz) spectral power and brain network features (nodal/local efficiency). Results showed valencespecific spatial patterns in spectral power, with significantly higher frontal gamma activity during positive states and increased right temporal gamma power during negative states. Network analysis revealed elevated local efficiency during positive emotions, indicating enhanced modular connectivity. Machine learning classification demonstrated higher accuracy for spectral power features (73.57% ± 2.30%) compared to nodal efficiency (69.51% ± 2.62%) and local efficiency (65.03% ± 1.33%), with key discriminators identified in frontal, temporal, and occipital regions. These findings suggest that localized high gamma activity provides more direct discriminative information for emotion recognition in VR than network topology metrics, advancing the understanding of neurophysiological responses in immersive VR environments.