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Accepted for/Published in: JMIR Research Protocols

Date Submitted: Sep 12, 2025
Date Accepted: Dec 17, 2025

The final, peer-reviewed published version of this preprint can be found here:

Evaluating Electroencephalogram-Based Predictive Model for Drowsiness Measurement to Reduce Accident Risk in Active Individuals: Protocol for a Preliminary Monocentric Study

Boitard C, Mazurie Z, Sadatnejad K, Coelho J, Sagaspe P, Lenoir J, Mattei J, Berthomier P, Brandwinder M, Philip P, Micoulaud Franchi JA, Berthomier C, Taillard J

Evaluating Electroencephalogram-Based Predictive Model for Drowsiness Measurement to Reduce Accident Risk in Active Individuals: Protocol for a Preliminary Monocentric Study

JMIR Res Protoc 2026;15:e83969

DOI: 10.2196/83969

PMID: 41701939

PMCID: 12912656

Study Protocol for Evaluating EEG-based Predictive Model for Drowsiness Measurement to Reduce Accident Risk in Active Individuals

  • Chloé Boitard; 
  • Zoé Mazurie; 
  • Khadijeh Sadatnejad; 
  • Julien Coelho; 
  • Patricia Sagaspe; 
  • Julie Lenoir; 
  • Julien Mattei; 
  • Pierre Berthomier; 
  • Marie Brandwinder; 
  • Pierre Philip; 
  • Jean-Arthur Micoulaud Franchi; 
  • Christian Berthomier; 
  • Jacques Taillard

ABSTRACT

Background:

Voluntary behaviors and socio-economic factors, such as social jetlag and shift work, can lead to insufficient or disrupted sleep, resulting in drowsiness in active individuals. In occupational and driving contexts, drowsiness poses a serious safety risk by impairing alertness, slowing reaction times, and increasing the likelihood of accidents. Developing automatic and easy to implement tools for drowsiness detection or prediction is essential in sleepy patient management or in high-risk environments where sustained vigilance is critical.

Objective:

This study aims to validate a continuous or predictive methods for assessing drowsiness using automated analysis of a limited number of electroencephalogram (EEG) channels.

Methods:

Designed as single-center, non-randomized, single-group, this study will evaluate drowsiness and cognitive performance in forty healthy volunteers exposed to two sleep deprivation conditions simulating real-world occupational scenarios. The primary outcome will be the Objective Sleepiness Scale (OSS) and its automated analysis, with a focus on its ability to measure objective wakefulness as assessed by the Maintenance of Wakefulness Test (MWT). Secondary outcomes will include multimodal resting-state EEG markers, subjective and objective sleepiness measures, performance on a simulated driving task, attention, executive function and vigilance assessments, as well as sleep quality, sleep quantity, and mind-wandering. The influence of sociodemographic and clinical variables on drowsiness measurement and prediction will also be systematically examined.

Results:

Subject recruitment began in March 2023 and was completed in May 2025, with a database lock and the start of data analysis in June 2025.

Conclusions:

By validating these novel EEG-based measures, this study aims to lay the groundwork for proactive drowsiness management strategies in occupational, transportation and clinical settings. Clinical Trial: French national ethical committee (Comité de Protection des Personnes Sud Est V) N° ID-RCB: 2021-A03234-37.


 Citation

Please cite as:

Boitard C, Mazurie Z, Sadatnejad K, Coelho J, Sagaspe P, Lenoir J, Mattei J, Berthomier P, Brandwinder M, Philip P, Micoulaud Franchi JA, Berthomier C, Taillard J

Evaluating Electroencephalogram-Based Predictive Model for Drowsiness Measurement to Reduce Accident Risk in Active Individuals: Protocol for a Preliminary Monocentric Study

JMIR Res Protoc 2026;15:e83969

DOI: 10.2196/83969

PMID: 41701939

PMCID: 12912656

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