# Time-variant Granger causality analysis for intuitive perception collision risk in driving scenario: an EEG study

## TL;DR

This study uses EEG to explore how experienced and novice drivers process collision risk intuitively, revealing differences in brain connectivity and decision-making.

## Contribution

An innovative experimental setup with immersive driving simulations and time-variant Granger causality analysis to study intuitive driving.

## Key findings

- Experienced drivers show increased activation in visual attention and decision-making networks.
- Experienced drivers have more stable and dispersed beta band connectivity compared to novice drivers.
- Novice drivers exhibit complex and less efficient connectivity, suggesting less optimized decision-making.

## Abstract

Intuition is a rapid and unconscious cognitive process that is widely utilized in driving scenario. The current study examines the neural mechanisms behind intuitive driving by performing a time-varying Granger causality analysis on source-domain EEG data. We construct an innovative experimental setup that utilizes immersive driving simulation videos to elicit intuitive decision-making alongside with neural activities. We performed Granger causality analysis on a sliding window basis that resulted in a directed connectivity model. By examining the node strength, we identify that the experienced drivers increase activation in intrinsic functional networks associated with visual attention and decision-making, which can be considered as the evidence for possessing better collision risk perception when compared to novice drivers. We also identify that experienced drivers exhibit a more stable and dispersed connectivity, especially in the beta band. In contrast, novice drivers exhibited more complex and less efficient connectivity, which can be interpreted as evidence of more efficient neural strategies for rapid decision-making in experienced drivers. This work not only advances the understanding of intuitive driving but also offers valuable insights for developing intelligent driving hazard perception systems. By targeting individual differences, we pave the way for personalized training programs to enhance driving safety and performance.