Deep Sleep Classification via EEG Signal Criticality: A Passive BCI Approach for Sleep-Improvement Neurofeedback
Abstract
Automated sleep staging is a fundamental application of passive Brain-Computer Interfaces (pBCI), decoding spontaneous neural states to enable closed-loop interventions independent of user intent. This study evaluates criticality features derived from Detrended Fluctuation Analysis (DFA) for the specific identification of deep sleep (N3). We analyzed $347,232$ EEG epochs from $290$ older women using UMAP manifold learning to visualize state transitions. Subsequently, six classifiers were benchmarked via 10-fold cross-validation, using balanced accuracy to determine the optimal "state-sensing" engine for neurofeedback.Naive Bayes achieved the highest mean balanced accuracy (87.17% ± 0.24%), significantly outperforming a fully connected deep neural network (FNN: 81.58%) and Random Forest (80.97%). Linear models (LDA: 57.21%; SVM: 51.01%) performed poorly, indicating that DFA-derived criticality features reside on a distinct, non-linear manifold. Probabilistic decoding of EEG criticality provides a high-accuracy sensing mechanism for pBCIs. This robust classification pipeline supports the development of state-dependent neurofeedback, such as targeted auditory stimulation, to enhance cognitive recovery.
Versions
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BibTeX
@misc{narebski2026deep,
title = {{Deep Sleep Classification via EEG Signal Criticality: A Passive BCI Approach for Sleep-Improvement Neurofeedback}},
author = {Stanisław Narębski and Tomasz Komendziński and Tomasz M. Rutkowski},
howpublished = {arXiv},
year = {2026},
month = jun,
doi = {10.48550/arxiv.2606.13017},
eprint = {2606.13017},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.13017},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Deep Sleep Classification via EEG Signal Criticality: A Passive BCI Approach for Sleep-Improvement Neurofeedback
AU - Stanisław Narębski
AU - Tomasz Komendziński
AU - Tomasz M. Rutkowski
PY - 2026
DA - 2026/06/11
PB - arXiv
DO - 10.48550/arxiv.2606.13017
UR - https://arxiv.org/abs/2606.13017
AB - Automated sleep staging is a fundamental application of passive Brain-Computer Interfaces (pBCI), decoding spontaneous neural states to enable closed-loop interventions independent of user intent. This study evaluates criticality features derived from Detrended Fluctuation Analysis (DFA) for the specific identification of deep sleep (N3). We analyzed $347,232$ EEG epochs from $290$ older women using UMAP manifold learning to visualize state transitions. Subsequently, six classifiers were benchmarked via 10-fold cross-validation, using balanced accuracy to determine the optimal "state-sensing" engine for neurofeedback.Naive Bayes achieved the highest mean balanced accuracy (87.17% ± 0.24%), significantly outperforming a fully connected deep neural network (FNN: 81.58%) and Random Forest (80.97%). Linear models (LDA: 57.21%; SVM: 51.01%) performed poorly, indicating that DFA-derived criticality features reside on a distinct, non-linear manifold. Probabilistic decoding of EEG criticality provides a high-accuracy sensing mechanism for pBCIs. This robust classification pipeline supports the development of state-dependent neurofeedback, such as targeted auditory stimulation, to enhance cognitive recovery.
ER -