Predicting driving performance from resting-state EEG using explainable gradient boosting
Abstract
Sleep deprivation substantially impairs driving performance, underscoring the need for Electroencephalography (EEG)-derived markers associated with driving-performance vulnerability under varying levels of sleepiness. In this study, resting-state EEG signals were recorded from 40 healthy participants following fragmented sleep or total sleep deprivation, simulating conditions of elevated sleep pressure. Recordings were collected across 12 sessions under eyes-open and eyes-closed conditions and acquired throughout the day and night to incorporate variability related to vigilance state, circadian phase, and sleep pressure. Participants subsequently completed a 20-minute driving simulation, with performance quantified using the standard deviation of lateral position (SDLP).We developed a subject-independent framework to predict driving performance (Safe vs. Risky) from resting-state EEG using a minimal number of channels under heterogeneous recording conditions. Spectral features were extracted from subject-specific frequency sub-bands aligned to individual Alpha peak frequencies, and Shapley additive explanations (SHAP)-based feature selection was employed to improve interpretability while maintaining predictive performance. SHAP were additionally used to characterize feature contributions to model predictions. Classification was performed using CatBoost, a gradient-boosted decision-tree algorithm.
The model achieved a subject-independent classification accuracy of 0.78 using EEG-derived features alone and 0.82 when age was included, despite substantial inter-and intra-individual variability. Delta-and Theta-band power at the Pz electrode during eyes-closed rest emerged among the features contributing most strongly to model predictions. Using only two EEG channels (Cz and Pz), the gradient-boosting models maintained comparable predictive performance. These findings support the feasibility of subject-independent, low-density EEG-based modeling for predicting driving-performance-related outcomes under controlled experimental conditions.
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BibTeX
@article{sadatnejad2026predicting,
title = {{Predicting driving performance from resting-state EEG using explainable gradient boosting}},
author = {Khadidjeh Sadatnejad and Christian Berthomier and Chloe Boitard and Zoé Mazurie and Julien Coehlo and Patricia Sagaspe and Jean Arthur Micoulaud-Franchi and Jacques Taillard},
journal = {Scientific Reports},
year = {2026},
month = aug,
doi = {10.1038/s41598-026-64300-6},
url = {https://hal.science/hal-05840477v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predicting driving performance from resting-state EEG using explainable gradient boosting
AU - Khadidjeh Sadatnejad
AU - Christian Berthomier
AU - Chloe Boitard
AU - Zoé Mazurie
AU - Julien Coehlo
AU - Patricia Sagaspe
AU - Jean Arthur Micoulaud-Franchi
AU - Jacques Taillard
PY - 2026
DA - 2026/08/18
JO - Scientific Reports
DO - 10.1038/s41598-026-64300-6
UR - https://hal.science/hal-05840477v1
AB - Sleep deprivation substantially impairs driving performance, underscoring the need for Electroencephalography (EEG)-derived markers associated with driving-performance vulnerability under varying levels of sleepiness. In this study, resting-state EEG signals were recorded from 40 healthy participants following fragmented sleep or total sleep deprivation, simulating conditions of elevated sleep pressure. Recordings were collected across 12 sessions under eyes-open and eyes-closed conditions and acquired throughout the day and night to incorporate variability related to vigilance state, circadian phase, and sleep pressure. Participants subsequently completed a 20-minute driving simulation, with performance quantified using the standard deviation of lateral position (SDLP).We developed a subject-independent framework to predict driving performance (Safe vs. Risky) from resting-state EEG using a minimal number of channels under heterogeneous recording conditions. Spectral features were extracted from subject-specific frequency sub-bands aligned to individual Alpha peak frequencies, and Shapley additive explanations (SHAP)-based feature selection was employed to improve interpretability while maintaining predictive performance. SHAP were additionally used to characterize feature contributions to model predictions. Classification was performed using CatBoost, a gradient-boosted decision-tree algorithm. The model achieved a subject-independent classification accuracy of 0.78 using EEG-derived features alone and 0.82 when age was included, despite substantial inter-and intra-individual variability. Delta-and Theta-band power at the Pz electrode during eyes-closed rest emerged among the features contributing most strongly to model predictions. Using only two EEG channels (Cz and Pz), the gradient-boosting models maintained comparable predictive performance. These findings support the feasibility of subject-independent, low-density EEG-based modeling for predicting driving-performance-related outcomes under controlled experimental conditions.
SN - 2045-2322
ER -