Study protocol for evaluating EEG-based predictive model for drowsiness measurement to reduce accident risk in active individuals Article Swipe
Chloe Boitard
,
Khadijeh Sadatnejad
,
Christian Berthomier
,
Julien Coehlo
,
Julie Lenoir
,
Patricia Sagaspe
,
Julien Mattei
,
Pierre Berthomier
,
Marie Brandewinder
,
Pierre Philip
,
Jean‐Arthur Micoulaud Franchi
,
Jacques Taillard
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.04.26.25326496
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.04.26.25326496
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 predictive, automatic and easy to implement tools for drowsiness detection is essential in high-risk environments where sustained vigilance is critical. This study aims to validate a practical and predictive method for assessing drowsiness using automated analysis of a limited number of electroencephalogram (EEG) channels. 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. 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.04.26.25326496
- https://www.medrxiv.org/content/medrxiv/early/2025/04/28/2025.04.26.25326496.full.pdf
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409876124
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409876124Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.04.26.25326496Digital Object Identifier
- Title
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Study protocol for evaluating EEG-based predictive model for drowsiness measurement to reduce accident risk in active individualsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-28Full publication date if available
- Authors
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Chloe Boitard, Khadijeh Sadatnejad, Christian Berthomier, Julien Coehlo, Julie Lenoir, Patricia Sagaspe, Julien Mattei, Pierre Berthomier, Marie Brandewinder, Pierre Philip, Jean‐Arthur Micoulaud Franchi, Jacques TaillardList of authors in order
- Landing page
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https://doi.org/10.1101/2025.04.26.25326496Publisher landing page
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https://www.medrxiv.org/content/medrxiv/early/2025/04/28/2025.04.26.25326496.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2025/04/28/2025.04.26.25326496.full.pdfDirect OA link when available
- Concepts
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Electroencephalography, Protocol (science), Computer science, Accident (philosophy), Psychology, Medicine, Psychiatry, Alternative medicine, Philosophy, Pathology, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.vigilance | 66, 176 |
| abstract_inverted_index.Developing | 48 |
| abstract_inverted_index.Sleepiness | 127 |
| abstract_inverted_index.accidents. | 47 |
| abstract_inverted_index.alertness, | 38 |
| abstract_inverted_index.attention, | 172 |
| abstract_inverted_index.conditions | 115 |
| abstract_inverted_index.drowsiness | 21, 30, 57, 81, 102, 195, 219 |
| abstract_inverted_index.groundwork | 216 |
| abstract_inverted_index.increasing | 43 |
| abstract_inverted_index.likelihood | 45 |
| abstract_inverted_index.management | 220 |
| abstract_inverted_index.multimodal | 157 |
| abstract_inverted_index.prediction | 198 |
| abstract_inverted_index.predictive | 77 |
| abstract_inverted_index.real-world | 117 |
| abstract_inverted_index.scenarios. | 119 |
| abstract_inverted_index.simulating | 116 |
| abstract_inverted_index.sleepiness | 164 |
| abstract_inverted_index.strategies | 221 |
| abstract_inverted_index.subjective | 161 |
| abstract_inverted_index.validating | 205 |
| abstract_inverted_index.volunteers | 109 |
| abstract_inverted_index.Maintenance | 148 |
| abstract_inverted_index.Wakefulness | 150 |
| abstract_inverted_index.deprivation | 114 |
| abstract_inverted_index.measurement | 196 |
| abstract_inverted_index.performance | 105, 166 |
| abstract_inverted_index.predictive, | 49 |
| abstract_inverted_index.wakefulness | 143 |
| abstract_inverted_index.assessments, | 177 |
| abstract_inverted_index.environments | 63 |
| abstract_inverted_index.individuals. | 24 |
| abstract_inverted_index.insufficient | 15 |
| abstract_inverted_index.occupational | 26, 118 |
| abstract_inverted_index.occupational, | 223 |
| abstract_inverted_index.resting-state | 158 |
| abstract_inverted_index.single-group, | 97 |
| abstract_inverted_index.single-center, | 95 |
| abstract_inverted_index.socio-economic | 3 |
| abstract_inverted_index.systematically | 202 |
| abstract_inverted_index.mind-wandering. | 186 |
| abstract_inverted_index.non-randomized, | 96 |
| abstract_inverted_index.transportation, | 224 |
| abstract_inverted_index.sociodemographic | 190 |
| abstract_inverted_index.electroencephalogram | 90 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 12 |
| citation_normalized_percentile.value | 0.15169599 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |