Robust multimodal mental workload classification: A cross-physiological condition machine learning approach
Our results highlight the need for training MW models under different physiological constraints and using multimodal datasets to improve robustness. (NCT05563688).
Authors
- Pierre Fabries (ORCID: https://orcid.org/0009-0003-5635-8220)
- Danielle Gomez-Mérino
- Nathalie Koulmann
- Fabien Sauvet (ORCID: https://orcid.org/0000-0002-4298-2319)
- Vincent Beauchamps (ORCID: https://orcid.org/0009-0003-6534-7347)
- Michaël Quiquempoix (ORCID: https://orcid.org/0000-0002-1445-4957)
- Pascal Van Beers
- M. Chennaoui
- Mathias Guillard
- Anaïs Pontiggia
- Clémentine Jacques
- Carine Malle
Institutions
- U.S. Army Research Institute of Environmental Medicine (US)
- Université Paris Cité (FR)
- Université Paris-Saclay (FR)
- Institut de Recherche et Coordination Acoustique Musique (FR)
- HIA du Val-de-Grâce à Paris (FR)
- Laboratoire Albert Fert (FR)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2026-01-15
- DOI
- https://doi.org/10.1016/j.cmpb.2026.109251
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00