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Cognitive load during driving: EEG microstate metrics are sensitive to task difficulty and predict safety outcomes
Siwei Ma1, Xuedong Yan1, Jac Billington2
1MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, PR China.
Accident; Analysis and Prevention
|September 5, 2024
Summary
EEG microstates analysis reveals distinct brain patterns during phone use while driving, differentiating cognitive load levels. These patterns predict driving safety outcomes, offering insights into distracted driving neural dynamics.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Human-Computer Interaction
Background:
- Phone use while driving impairs cognitive function and increases accident risk.
- Existing electroencephalography (EEG) methods cannot differentiate varying cognitive load levels.
- The predictive power of EEG for driving safety outcomes during critical events remains unexplored.
Purpose of the Study:
- To investigate EEG microstates' ability to differentiate cognitive load during driving with phone use.
- To determine if EEG microstates can predict safety outcomes in critical driving scenarios.
- To explore the neural dynamics of distracted driving.
Main Methods:
- A driving simulation experiment was conducted with three phone conditions (none, hands-free, handheld) and two task difficulties (simple, complex arithmetic).
- Both conventional EEG spectral power and EEG microstates were analyzed.
- EEG data were collected before and during a simulated rear-end collision.
Main Results:
- EEG microstates, unlike spectral power, showed distinct patterns differentiating cognitive load levels during phone use.
- A specific EEG microstate pattern indicated heightened auditory focus and reduced attention reorientation ability.
- This pattern monotonically increased with task difficulty and was sensitive to phone use type (handheld vs. hands-free).
- Pre-braking EEG microstates significantly improved predictions of post-braking safety outcomes (minimum time headway).
- EEG microstates were more predictive of safety outcomes than task difficulty, highlighting individual differences.
Conclusions:
- EEG microstates effectively measure cognitive load during distracted driving, unlike spectral power analysis.
- EEG microstate patterns can predict driving safety, indicating impaired brain states.
- This research provides a novel method for evaluating cognitive load from in-vehicle systems and understanding distracted driving.
