Disentangling EEG Fingerprinting and Sleep Biomarkers Using Generalized Weighted Ordinal Patterns.

Overview

1. Departamento de Física, Universidad Nacional del Sur, Bahía Blanca 8000, Argentina; (C.D.D.); (A.A.); (F.R.I.)
2. Instituto de Física del Sur, Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Bahía Blanca 8000, Argentina
3. Laboratorio de Ciencias de las Imágenes, Departamento de Ingeniería Eléctrica y Computadoras, Universidad Nacional del Sur-Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Bahía Blanca 8000, Argentina

Abstract

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 ρ=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.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• physionet.org/content/sleep-edfx, at PhysioNet; found in “Data Availability Statement”

Data Availability Statement

The data analyzed in this study are publicly available from the Sleep-EDF Database Expanded through PhysioNet[https://physionet.org/content/sleep-edfx/1.0.0/], accessed on 23 July 2026. The models generated during the current study are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 1 funder, 22 references.

Cite

This paper

Duarte, C. D., Arlenghi, A., Iaconis, F. R., Gasaneo, G., & Delrieux, C. (2026). Disentangling EEG Fingerprinting and Sleep Biomarkers Using Generalized Weighted Ordinal Patterns. Brain sciences, 16(8), 793. https://doi.org/10.3390/brainsci16080793

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