BCI ReportFive-stage sleep
EESM19 (Ear-EEG Sleep Monitoring 2019, OpenNeuro ds005185) is a home sleep dataset from Aarhus University: 20 participants each slept four nights with dry-contact ear-EEG plus partial polysomnography, manually sleep-scored, and 10 of them added 12 ear-EEG-only nights. First published by Mikkelsen et al. in Scientific Reports (2019).
Also known as EESM19 · Ear-EEG Sleep Monitoring 2019 · ds005185 · Mikkelsen et al. (2019)
Description sources openneuro.org · pmc.ncbi.nlm.nih.gov · doi.org
Each figure comes from the reviewed download linked in its group. One row is above another only where their 95% intervals do not overlap.
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Spectral ridge | Five stages · balanced scalp-EEG sample | Balanced accuracy | 72.2%70.7%–73.8% | 20 |
| LaBraM | Five stages · balanced scalp-EEG sample | Balanced accuracy | 71.1%69.0%–73.2% | 20 |
| CBraMod | Five stages · balanced scalp-EEG sample | Balanced accuracy | 71.3%68.7%–73.7% | 20 |
| EEGNet | Five stages · balanced scalp-EEG sample | Balanced accuracy | 56.5%54.9%–58.3% | 20 |
| ShallowFBCSPNet | Five stages · balanced scalp-EEG sample | Balanced accuracy | 55.0%53.1%–56.8%One fixed seed and training budget, as the EEGNet cell | 20 |
| Deep4Net | Five stages · balanced scalp-EEG sample | Balanced accuracy | 45.2%42.7%–47.7%One fixed seed and training budget, as the EEGNet cell | 20 |
| CSP+LDA (one-vs-rest CSP) | Five stages · balanced scalp-EEG sample | Balanced accuracy | 50.1%46.9%–53.1%One-vs-rest CSP: one binary CSP per stage, 30 log-variance features, one shrinkage LDA; broadband as in the published mi-rest recipe; a new code path, not a tuned CSP. | 20 |
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| REVE Base | Frozen encoder + ridge head | Balanced accuracy | 74.6%72.9%–76.4% | 20 |
| REVE Large | Frozen encoder + ridge head | Balanced accuracy | 76.6%75.2%–78.0% | 20 |
| LUNA Base | Frozen encoder + ridge head | Balanced accuracy | 67.5%65.3%–69.5% | 20 |
| LUNA Large | Frozen encoder + ridge head | Balanced accuracy | 72.4%70.4%–74.0% | 20 |
| BrainOmni Base | Frozen encoder + ridge head | Balanced accuracy | 67.7%65.8%–69.5% | 20 |
| CodeBrain | Frozen encoder + ridge head | Balanced accuracy | 70.7%68.8%–72.7% | 20 |
| EEGMamba | Frozen encoder + ridge head | Balanced accuracy | 64.1%61.6%–66.4% | 20 |
| ST-EEGFormer Base | Frozen encoder + ridge head | Balanced accuracy | 77.3%75.6%–78.8% | 20 |
| ST-EEGFormer Large | Frozen encoder + ridge head | Balanced accuracy | 79.6%78.2%–81.0% | 20 |
| eeg-fm-masking MAE (r=9 cm, L=2) | Frozen encoder + ridge head | Balanced accuracy | 67.9%65.4%–70.4% | 20 |
| ERP-FM Base | Frozen encoder + ridge head | Balanced accuracy | 71.0%69.5%–72.6% | 20 |
| SingLEM | Frozen encoder + ridge head | Balanced accuracy | 35.3%33.7%–37.0% | 20 |
| ZUNA 1.1 | Frozen encoder + ridge head | Balanced accuracy | 60.1%57.8%–62.3%Pretraining exposure: unknown: the authors do not list their pretraining data closely enough to decide. Model card: research use only, not for diagnosis or clinical use. | 20 |
| eeg-fm-masking JEPA (r=9 cm, L=2) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 70.3%68.0%–72.7% | 20 |
| eeg-fm-masking MAE (r=one channel, L=1) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 65.2%63.0%–67.3% | 20 |
| eeg-fm-masking JEPA (r=one channel, L=1) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 68.3%65.7%–70.8% | 20 |
Weights terms: REVE under the REVE Responsible Use License v1.0 (model versions: REVE Base @ dc2a075c · REVE Large @ 317531c7); LUNA under CC BY-ND 4.0, with no modified weights shared; BrainOmni, EEGMamba, ST-EEGFormer and SingLEM under MIT; CodeBrain and ZUNA under Apache-2.0; eeg-fm-masking under CC-BY-4.0; ERP-FM under CC BY-NC-SA 4.0, non-commercial. No model’s authors endorse these results. Every weights licence and its terms →
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| EEGPT | Frozen encoder + ridge head | Balanced accuracy | 75.5%73.9%–77.1% | 20 |
Every core window is shorter than EEGPT’s 4-second pretraining window: 1 or 2 seconds, and sleep as fifteen 2-second segments. The encoder computes each 0.25-second patch the same way at any length; a shorter window gives fewer patches to average.
The readout is the site’s fixed probe on the frozen summary tokens, as for the published LaBraM and CBraMod rows: ridge (alpha 100) on 2,048 pooled values, and on idle a per-person logistic head. It is not the authors’ downstream probe, a learnable spatial filter with a linear head, and the windows are fed without the average reference EEGPT was pretrained with.
EEGPT: the checkpoint’s licence is unresolved, and no original or converted weights are distributed; Apache-2.0 covers the repository’s code only. Its licence and terms →
Credit Kaare B. Mikkelsen et al. · Accurate whole-night sleep monitoring with dry-contact ear-EEG (2019), doi:10.1038/s41598-019-53115-3; OpenNeuro ds005185 v1.0.2. Processed mirror: Zachary1150/EESM19-Processed.
CC0-1.0 declared by upstream and mirror ↗ · Dataset record ↗
BCI Report does not redistribute any recording. These are aggregate measurements computed by BCI Report under the licence above; the data remain their creators’.
BCI Report (2026). EESM19 scalp subset: EEG decoding results. https://bci.report/datasets/eesm19/
Figures from releases core-matrix-update-20261106 (2026-11-06), eegpt-core-update-20261013 (2026-10-13), shared-representation-update-20261007 (2026-10-07), foundation-models-update-20261004 (2026-10-04), research-preview-20260920 (2026-09-20). Cite the upstream dataset as well: its credit is on this page.
Every release is archived on Zenodo: doi:10.5281/zenodo.23123296. BibTeX for the site and its releases → · CITATION.cff ↗