{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:14:26Z","timestamp":1785413666038,"version":"3.56.0"},"reference-count":24,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005740","name":"Science and Technology Office of the Universidad Nacional del Sur","doi-asserted-by":"publisher","award":["PGI 24\/K093"],"award-info":[{"award-number":["PGI 24\/K093"]}],"id":[{"id":"10.13039\/501100005740","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005740","name":"Science and Technology Office of the Universidad Nacional del Sur","doi-asserted-by":"publisher","award":["PGI 24\/F089"],"award-info":[{"award-number":["PGI 24\/F089"]}],"id":[{"id":"10.13039\/501100005740","id-type":"DOI","asserted-by":"publisher"}]},{"award":["PGI 24\/K093"],"award-info":[{"award-number":["PGI 24\/K093"]}],"id":[{"id":"https:\/\/ror.org\/028crwz56","id-type":"ROR","asserted-by":"publisher"}]},{"award":["PGI 24\/F089"],"award-info":[{"award-number":["PGI 24\/F089"]}],"id":[{"id":"https:\/\/ror.org\/028crwz56","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sci.","Brain Sciences"],"abstract":"<jats:p>Background: Electroencephalographic (EEG) recordings simultaneously contain information about neurophysiological dynamics and subject-specific characteristics. While this duality may enable biomarker discovery and individual identification, it also raises concerns that machine-learning models may achieve high predictive performance by exploiting subject identity rather than physiologically relevant information. Methods: In this study, we investigated whether generalized weighted ordinal patterns (GWOP), a statistical-complexity representation incorporating both temporal ordering and amplitude fluctuations, support sleep-stage classification while minimizing identity-related confounding. Sleep EEG recordings from 31 healthy subjects were segmented into 30-s epochs and represented using 3150 GWOP features derived from multiple embedding dimensions, time delays, and entropic indices. XGBoost classifiers were evaluated under intra-subject and inter-subject validation schemes to quantify the impact of EEG fingerprinting on sleep-stage classification performance. An additional subject-identification analysis was conducted using the same feature representation. Results: Sleep-stage classification generalized well to previously unseen subjects, with accuracy decreasing only from 79.2% to 75.8% between intra-subject and inter-subject evaluations. Feature-importance analysis using SHAP revealed an almost perfect correspondence between the features driving classification in both validation schemes (Spearman \u03c1=0.998). Conclusions: While this suggests that the models effectively generalize across subjects without being heavily confounded by individual identities, it indicates a framework of partial separation rather than complete orthogonality across the global feature space. In contrast, GWOP features also supported subject identification with 63.9% accuracy across the 31 individuals, demonstrating that GWOP preserve substantial fingerprinting information. The most informative features for subject identification showed little overlap with those governing sleep-stage classification, suggesting a partial separation between identity-related and biomarker-related information within the same feature space. These findings suggest that EEG fingerprinting and biomarker extraction are not necessarily competing objectives and support GWOP-based statistical-complexity measures as a promising proof-of-concept framework for robust sleep EEG analysis, serving as a foundation for future scale-up precision-neuroscience applications.<\/jats:p>","DOI":"10.3390\/brainsci16080793","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T07:55:41Z","timestamp":1785225341000},"page":"793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Disentangling EEG Fingerprinting and Sleep Biomarkers Using Generalized Weighted Ordinal Patterns"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8034-4987","authenticated-orcid":false,"given":"Cristina Daiana","family":"Duarte","sequence":"first","affiliation":[{"name":"Departamento de F\u00edsica, Universidad Nacional del Sur, Bah\u00eda Blanca 8000, Argentina"},{"name":"Instituto de F\u00edsica del Sur, Consejo Nacional de Investigaciones Cient\u00edficas y Tecnol\u00f3gicas (CONICET), Bah\u00eda Blanca 8000, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6905-6665","authenticated-orcid":false,"given":"Albertina","family":"Arlenghi","sequence":"additional","affiliation":[{"name":"Departamento de F\u00edsica, Universidad Nacional del Sur, Bah\u00eda Blanca 8000, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2373-5793","authenticated-orcid":false,"given":"Francisco Ramiro","family":"Iaconis","sequence":"additional","affiliation":[{"name":"Departamento de F\u00edsica, Universidad Nacional del Sur, Bah\u00eda Blanca 8000, Argentina"},{"name":"Instituto de F\u00edsica del Sur, Consejo Nacional de Investigaciones Cient\u00edficas y Tecnol\u00f3gicas (CONICET), Bah\u00eda Blanca 8000, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6200-2851","authenticated-orcid":false,"given":"Gustavo","family":"Gasaneo","sequence":"additional","affiliation":[{"name":"Departamento de F\u00edsica, Universidad Nacional del Sur, Bah\u00eda Blanca 8000, Argentina"},{"name":"Instituto de F\u00edsica del Sur, Consejo Nacional de Investigaciones Cient\u00edficas y Tecnol\u00f3gicas (CONICET), Bah\u00eda Blanca 8000, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2727-8374","authenticated-orcid":false,"given":"Claudio","family":"Delrieux","sequence":"additional","affiliation":[{"name":"Laboratorio de Ciencias de las Im\u00e1genes, Departamento de Ingenier\u00eda El\u00e9ctrica y Computadoras, Universidad Nacional del Sur-Consejo Nacional de Investigaciones Cient\u00edficas y Tecnol\u00f3gicas (CONICET), Bah\u00eda Blanca 8000, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"giac082","DOI":"10.1093\/gigascience\/giac082","article-title":"Statistical quantification of confounding bias in machine learning models","volume":"11","author":"Spisak","year":"2022","journal-title":"GigaScience"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1038\/s41746-019-0178-x","article-title":"Detecting the impact of subject characteristics on machine learning-based diagnostic applications","volume":"2","author":"Pratap","year":"2019","journal-title":"npj Digit. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103748","DOI":"10.1016\/j.compbiomed.2020.103748","article-title":"EEG fingerprinting: Subject-specific signature based on the aperiodic component of power spectrum","volume":"120","author":"Demuru","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Brookshire, G., Kasper, J., Blauch, N.M., Wu, Y.C., Glatt, R., Merrill, D.A., Gerrol, S., Yoder, K.J., Quirk, C., and Lucero, C. (2024). Data leakage in deep learning studies of translational EEG. Front. Neurosci., 18.","DOI":"10.3389\/fnins.2024.1373515"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"120637","DOI":"10.1016\/j.neuroimage.2024.120637","article-title":"The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device","volume":"294","author":"Lin","year":"2024","journal-title":"Neuroimage"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"171025","DOI":"10.1109\/ACCESS.2025.3605614","article-title":"Deep Learning Approaches for EEG-Based Biometrics: A Systematic Review","volume":"13","author":"Albaiati","year":"2025","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S57","DOI":"10.1016\/S0893-133X(01)00320-7","article-title":"Individual \u2018fingerprints\u2019 in human sleep EEG topography","volume":"25","author":"Finelli","year":"2001","journal-title":"Neuropsychopharmacology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/ana.21434","article-title":"The electroencephalographic fingerprint of sleep is genetically determined: A twin study","volume":"64","author":"Marzano","year":"2008","journal-title":"Ann. Neurol."},{"key":"ref_9","first-page":"1","article-title":"Mindid: Person identification from brain waves through attention-based recurrent neural network","volume":"2","author":"Zhang","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_10","first-page":"3946740","article-title":"Threats and Mitigation Strategies for Electroencephalography-Based Person Authentication","volume":"2025","author":"Unnisa","year":"2025","journal-title":"Int. J. Telemed. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"597","DOI":"10.3390\/signals5030033","article-title":"How time window influences biometrics performance: An eeg-based fingerprint connectivity study","volume":"5","author":"Didaci","year":"2024","journal-title":"Signals"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: A natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bandt, C. (2017). A new kind of permutation entropy used to classify sleep stages from invisible EEG microstructure. Entropy, 19.","DOI":"10.3390\/e19050197"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Berger, S., Schneider, G., Kochs, E.F., and Jordan, D. (2017). Permutation entropy: Too complex a measure for EEG time series?. Entropy, 19.","DOI":"10.3390\/e19120692"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103105","DOI":"10.1063\/5.0107427","article-title":"Generalized weighted permutation entropy","volume":"32","author":"Stosic","year":"2022","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_16","first-page":"4765","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/10.867928","article-title":"Analysis of a sleep-dependent neuronal feedback loop: The slow-wave microcontinuity of the EEG","volume":"47","author":"Kemp","year":"2000","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","first-page":"e215","article-title":"PhysioNet: Componentes de um novo recurso de pesquisa para sinais fisiol\u00f3gicos complexos","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"31001","DOI":"10.1209\/0295-5075\/ac6a72","article-title":"20 years of ordinal patterns: Perspectives and challenges","volume":"138","author":"Leyva","year":"2022","journal-title":"Europhys. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Duarte, C.D., Meo, M.M., Iaconis, F.R., Wainselboim, A., Gasaneo, G., and Delrieux, C. (2025). A Permutation Entropy Method for Sleep Disorder Screening. Brain Sci., 15.","DOI":"10.20944\/preprints202505.0777.v1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"022911","DOI":"10.1103\/PhysRevE.87.022911","article-title":"Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information","volume":"87","author":"Fadlallah","year":"2013","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Duarte, C.D., Pacheco, M., Iaconis, F.R., Rosso, O.A., Gasaneo, G., and Delrieux, C.A. (2025). Statistical complexity analysis of sleep stages. Entropy, 27.","DOI":"10.3390\/e27010076"},{"key":"ref_23","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From Local Explanations to Global Understanding with Explainable AI for Trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/8\/793\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T08:22:22Z","timestamp":1785226942000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/8\/793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,28]]},"references-count":24,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["brainsci16080793"],"URL":"https:\/\/doi.org\/10.3390\/brainsci16080793","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,28]]}}}