EEG based sleep staging

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Most implemented papers archive 2025-07-28

7 shown of 7 papers with code (10 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Structure-Preserving Transformers for Sequences of SPD Matrices
  14 Sep 2023 2 repositories listed
  In recent years, Transformer-based auto-attention mechanisms have been successfully applied to the analysis of a variety of context-reliant data types, from texts to images and beyond, including data from non-Euclidean…
• Uncovering the structure of clinical EEG signals with self-supervised learning
  31 Jul 2020 2 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.
• EEG aided boosting of single-lead ECG based sleep staging with Deep Knowledge Distillation
  18 Nov 2022 1 repository listed
  In order to take advantage of both the modalities, transferring knowledge from EEG to ECG is a reasonable approach, ultimately boosting the performance of ECG based sleep staging.
• A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data
  27 Oct 2022 1 repository listed
  Sleep plays a crucial role in the well-being of human lives.
• A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging
  14 Dec 2021 1 repository listed
  This demonstrates the viability of KD for performance improvement of single-channel ECG based sleep staging in 4-class(W-L-D-R) and 3-class(W-N-R) classification.
• Self-supervised Contrastive Learning for EEG-based Sleep Staging
  16 Sep 2021 1 repository listed Syntology ran 1 of 7 samples · 6 unverified
  In detail, the network's performance depends on the choice of transformations and the amount of unlabeled data used in the training process of self-supervised learning.
• ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
  9 Jul 2021 1 repository listed
  Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.