EEG-based driving intuition and collision anticipation using joint temporal-frequency multi-layer dynamic brain network.

ABSTRACT: Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers. The results show that brain region activity is more stable in the advanced phase of intuition training compared to the initial phase. Particularly in the alart state task, the JTF-MDBN demonstrated stronger connection strength.

SUBMITTER: Liang J 

PROVIDER: S-EPMC11233801 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature