Robust EEG-based cross-site and cross-protocol classification of states of consciousness
Open accessAbstract
Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.
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EEG/MEG signals, generated by synchronous neuronal firing, are the most accessible measure of the brain's EM field and strongly correlate with conscious states, as evidenced by clinical use in brain-damaged patients and general anesthesia.
“The strong correlation between EEG signals and conscious states is evidenced by the widespread clinical use of EEG to assess the level of consciousness and awareness in brain-damaged patients (Engemann et al., 2018) and in general anesthesia (Musialowicz and Lahtinen, 2014).”
From Consciousness: Matter or EMF? (2023) · cites this paper as Engemann et al., 2018
The study summary (p. 3) reports that EEG markers distinguish states of consciousness in patients with disorders of consciousness, and the Introduction (p. 5) describes EEG as suitable for bedside assessment. This supports the link between EEG and consciousness assessment in brain-damaged patients, but the paper does not establish that EEG is already in widespread clinical use for that purpose. The separate anesthesia citation is not assessed here. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe re-formulation of the diagnostic criteria for ASD in DSM-5, which is similar to the criteria in ICD-11, contains several changes from previous editions that were based on good empirical and clinical evidence.
“The re-formulation of the diagnostic criteria for ASD in the DSM-5 (Table 1) 121 , which is similar to the criteria in ICD-11 122 , contains several changes from previous editions that were based on good empirical and clinical evidence 123”
From Autism spectrum disorder (2020) · cites this paper as [121]
The Introduction (p. 4) discusses diagnostic assessment of disorders of consciousness, and the Participants section (p. 6) describes diagnoses of unresponsive wakefulness syndrome and minimally conscious state. Searches for ASD, DSM-5, ICD-11, and changes to autism diagnostic criteria found no passage addressing whether those changes were based on empirical and clinical evidence. (LLM, conf=0.78)
Experimental — accuracy not yet measuredOnly limited studies of the correlation between EEG and behavior have been performed in primates, cats, horses, and a scattering of other animals.
“Unfortunately, only limited studies of the correlation between EEG and behavior have been performed in primates (Attaheri et al., 2015), cats (Engemann et al., 2018), horses (de Camp et al., 2020), and a scattering of other animals (Klemm, 1992);”
From Consciousness: Matter or EMF? (2023) · cites this paper as Engemann et al., 2018
The citation is attached to the claim about limited EEG–behavior studies in cats. The Introduction (pp. 4–5) and Participants section (p. 6) discuss EEG and behavioral assessment in human patients, but the passages returned for searches on cats, feline studies, and EEG–behavior correlations do not address studies in cats or how many exist. (LLM, conf=0.74)
Experimental — accuracy not yet measured
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“, 2015), cats (Engemann et al., 2018), horses (de Camp et al.”
BackgroundAdama, S., & Bogdan, M. (2026) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2026.1797090 · Cited by 1 · see citing works
- Authors
- Engemann, D. A., Raimondo, F., King, J.-R., Rohaut, B., Louppe, G., Faugeras, F., Annen, J., Cassol, H., Gosseries, O., Fernandez-Slezak, D., Laureys, S., Naccache, L., Dehaene, S., & Sitt, J. D.
- Affiliations
- Université Paris-Sud (FR)
- Centre National de la Recherche Scientifique (FR)
- New York University (US)
- University of Liège (BE)
- Coma Science Group, GIGA Consciousness, University and University Hospital of Liège, Liège, Belgium
- Laboratorio de Inteligencia Artificial Aplicada, Departamento de Computación FCEyN, UBA, Argentina
- Corresponding author
- Engemann, Denis A
- Raimondo, Federico
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-10-04
- Volume
- 141
- Issue
- 11
- Pages
- 3179-3192
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- OpenAlex
- W2895368692
- Cited by
- 329
- References
- 66
- Citations by year
- 2026: 35 · 2025: 38 · 2024: 38 · 2023: 59 · 2022: 52 · 2021: 27 · 2020: 42 · 2019: 34 · 2018: 4
- FWCI
- 15.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Brain (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- ORBi (University of Liège) (submittedVersion)
- Conicet (submittedVersion, cc-by-nc-sa)
- Funders
- European Union's Horizon 2020 Framework Programme for Research and Innovation (720270)
- Luminous project (EU-H2020-fetopen-ga686764)
- BIAL Foundation
- Mind Science Foundation
- Amazon Web Services (ERCYStG-263584)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Epidemiology › Traumatic Brain Injury Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- disorders of consciousness, vegetative state, minimally conscious state, cross-site generalization, multivariate pattern classification, decision tree ensembles, label noise robustness
- MeSH terms
- Adult, Consciousness, Consciousness Disorders, Electroencephalography, Female, Humans, Information Theory, Male, Middle Aged, Wakefulness, Entropy, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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