{
  "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.\n\nThe 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Khadidjeh Sadatnejad"
    },
    {
      "name": "Christian Berthomier"
    },
    {
      "name": "Chloe Boitard"
    },
    {
      "name": "Zoé Mazurie"
    },
    {
      "name": "Julien Coehlo"
    },
    {
      "name": "Patricia Sagaspe"
    },
    {
      "name": "Jean Arthur Micoulaud-Franchi"
    },
    {
      "name": "Jacques Taillard"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c033pe3c5g54g.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-64300-6",
  "fulltext_url": null,
  "id": "c033pe3c5g54g",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://hal.science/hal-05840477v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7170137744",
  "pdf_url": "https://hal.science/hal-05840477/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-18",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/c033pe3c5g54g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "hal-05840477",
      "source": "hal",
      "url": "https://hal.science/hal-05840477v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|EEG and Brain-Computer Interfaces",
      "subfield": "neuroscience"
    }
  ],
  "title": "Predicting driving performance from resting-state EEG using explainable gradient boosting",
  "title_text": "Predicting driving performance from resting-state EEG using explainable gradient boosting",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/c033pe3c5g54g",
  "venue": "Scientific Reports",
  "versions": []
}