mlsanityData to 14 Oct 2026

Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks

arXiv 2410.19819 ↗First posted 18 Oct 2024eess.SP cs.LGcited by 0

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Abstract (arXiv)

Purpose: In sleep medicine, assessing the evolution of a subject's sleep often involves the costly manual scoring of electroencephalographic (EEG) signals. In recent years, a number of Deep Learning approaches have been proposed to automate this process, mainly by extracting features from said signals. However, despite some promising developments in related problems, such as Brain-Computer Interfaces, analyses of the covariances between brain regions remain underutilized in sleep stage scoring.Methods: Expanding upon our previous work, we investigate the capabilities of SPDTransNet, a Transformer-derived network designed to classify sleep stages from EEG data through timeseries of covariance matrices. Furthermore, we present a novel way of integrating learned signal-wise features into said matrices without sacrificing their Symmetric Definite Positive (SPD) nature.Results: Through comparison with other State-of-the-Art models within a methodology optimized for class-wise performance, we achieve a level of performance at or beyond various State-of-the-Art models, both in single-dataset and - particularly - multi-dataset experiments.Conclusion: In this article, we prove the capabilities of our SPDTransNet model, particularly its adaptability to multi-dataset tasks, within the context of EEG sleep stage scoring - though it could easily be adapted to any classification task involving timeseries of covariance matrices.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 68 references that are in the map. First posted on arXiv 18 Oct 2024.

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29 of 68 references.

References · 29

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  7. 2018SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Stagingcited by 91
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  11. 2017A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriescited by 58
  12. 2019A neural network based on SPD manifold learning for skeleton-based hand gesture recognitioncited by 46
17 more
  1. 2022MAtt: A Manifold Attention Network for EEG Decodingcited by 44
  2. 2021Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directionscited by 43
  3. 2018Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classificationcited by 42
  4. 2021Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classificationcited by 37
  5. 2019Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep stagingcited by 33
  6. 2021SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Stagingcited by 32
  7. 2021Universal Approximation Under Constraints is Possible with Transformerscited by 32
  8. 2021RobustSleepNet: Transfer learning for automated sleep staging at scalecited by 24
  9. 2021Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?cited by 24
  10. 2023L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Stagingcited by 21
  11. 2017Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoringcited by 15
  12. 2022Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformerscited by 13
  13. 2022Multi-manifold Attention for Vision Transformerscited by 12
  14. 2022ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregationcited by 5
  15. 2023A Strong and Simple Deep Learning Baseline for BCI MI Decodingcited by 5
  16. 2023Structure-Preserving Transformers for Sequences of SPD Matricescited by 4
  17. 2015Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matricescited by 2

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