Reflection of phobic scenarios in virtual reality based on EEG data and intelligent video streaming

Introduction: Throughout everyday life, people encounter potentially traumatic events. Under the influence of both mental and physical factors, chronic neuropsychic stress develops, leading to changes in the nervous system's excitability threshold, thereby disrupting the subject's functional comfort.

This research aimed to analyze the dynamics of the functional state (FS) of healthy subjects exposed to phobic virtual reality scenarios using EEG recordings and muscle activity analysis using intelligent video streaming analysis.

Methods: A total of 20 volunteers, 12 women and 8 men, aged 19–20 years were selected. Immersion in a dynamic virtual environment was achieved using a portable personal computer and a VR program simulating heights and crowd danger. Psychoemotional state and responses to the simulation were recorded using a 21-channel EEG. At the same time, an approach was used to assess muscle movements of linear segments of the body and face using a commercial smartphone camera, followed by processing using proprietary software. Modern AI methods make it possible to classify FS and behavioral activity based on the analysis of external features obtained from a video camera.

Outcomes: Virtual reality exposure to heights (a multi-floor building landing) was accompanied by an increase in the average amplitude of the EEG alpha rhythm by 3.8±1.4%, theta rhythm by 9.1±0.7%, delta rhythm by 4.7±2.6%, and beta rhythm by 34.1±6.1%, indicating generalized cerebral irritation.

Intelligent video monitoring displays a motor activity algorithm and acceptable results for respiratory activity at an average respiratory rate. The motion detection algorithm demonstrated 100% accuracy in both detecting body part movements and determining the direction of movement.

Conclusion: This finding opens the door to optimizing currently used algorithms for the automatic detection of phobic disorders based on EEG data, particularly AI algorithms.