Modeling subjective fear using skin conductance: a preliminary study in virtual reality

Area of Science:

• Psychophysiology
• Virtual Reality Applications
• Biomedical Signal Processing

Background:

• Accurate measurement of fear perception is crucial for assessing treatments like exposure therapy for phobias and PTSD.
• Virtual reality (VR) offers a controlled environment for inducing and studying fear responses.
• Skin conductance (SC) is a physiological indicator of autonomic nervous system arousal.

Purpose of the Study:

• To develop a novel VR scenario for inducing fear and measuring physiological responses.
• To analyze skin conductance (SC) signals to quantify autonomic responses to fear stimuli.
• To model subjective fear perception using extracted SC features.

Main Methods:

• Eighteen subjects experienced a fear-inducing VR scenario while their SC was recorded.
• The cvxEDA algorithm processed SC signals to extract relevant features.
• LASSO linear regression was employed to model subjective fear scores using SC features.

Main Results:

• The standard deviation of the tonic SC component was identified as the optimal predictor of subjective fear.
• This feature significantly contributed to the model (p = 0.007) with an R-squared value of 0.3337.
• Higher subjective fear correlated with greater variability in the sympathetic tone.

Conclusions:

• The standard deviation of the tonic SC component is a reliable physiological marker for subjective fear intensity.
• VR-induced fear paradigms combined with SC analysis can effectively evaluate fear perception.
• Findings suggest a link between intense subjective fear and sympathetic nervous system instability.