| --- |
| library_name: braindecode |
| license: mit |
| pipeline_tag: feature-extraction |
| base_model: braindecode/STEEGFormer-small |
| tags: |
| - braindecode |
| - CORTEG |
| - ieeg |
| - ecog |
| - foundation-model |
| - pytorch_model_hub_mixin |
| datasets: |
| - nm000245 |
| - nm000172 |
| - nm000139 |
| - nm000135 |
| - Kaya2018 |
| - Won2022 |
| - nm000129 |
| - nm000339 |
| - SparrKULee |
| - SEED-V |
|
|
| --- |
| |
| # CORTEG, ST-EEGFormer small backbone |
|
|
| The starting point of CORTEG ([Yang et al., 2026](https://arxiv.org/abs/2605.10337)), |
| ST-EEGFormer small adapted to intracranial recordings, for |
| [`braindecode.models.CORTEG`](https://braindecode.org/stable/generated/braindecode.models.CORTEG.html): |
| the ST-EEGFormer small encoder ([`checkpoint-300.pth`](https://github.com/LiuyinYang1101/STEEGFormer/releases/download/ST-EEGFormer-small/checkpoint-300.pth), |
| sha256 `98d2848abdaea7a143d399c07f67f8782e07dd66e7c309ddb058c7158a8a8b02`, through its conversion |
| [braindecode/STEEGFormer-small](https://huggingface.co/braindecode/STEEGFormer-small) at revision |
| `633c9c0d`), 25.3M parameters. The |
| high-gamma patch embedding, which the scalp model does not have, is warm-started |
| from the low-frequency one, linearly interpolated from 16 to 25 samples, as the |
| authors' `build_model` does ([LiuyinYang1101/CORTEG](https://github.com/LiuyinYang1101/CORTEG/tree/caafb78fbebdd9448814f96d7e61083a3eb62663), |
| `experiments/run_regression_hilo_clean.py`); it matches theirs to |
| 0.0e+00 in float32 on CPU. `convert_corteg_checkpoint.py` in this |
| repository reproduces the conversion. |
|
|
|
|
| <!-- braindecode:training-data --> |
| ## Training data |
|
|
| The data that shaped these weights. The ids are the `datasets:` field of this card, |
| which braindecode reads into `model.card_data.datasets` when you call |
| `from_pretrained` (MOABB class names for MOABB datasets, OpenNeuro / NEMAR |
| accessions, Hub ids otherwise). `nm`/`on` ids are datasets on [NEMAR](https://nemar.org) |
| (`on` = NEMAR's copy of the OpenNeuro dataset with the same number). Rows marked |
| `private:` are data the authors did not release; they are not in `datasets:`. |
|
|
| | `datasets:` id | Dataset | Stage | Modality | |
| |---|---|---|---| |
| | [`nm000245`](https://nemar.org/dataset/nm000245) | [EEG-MI-BCI: Cho et al. 2017, EEG datasets for motor imagery BCI (52 subjects, 64 ch, left/right-hand MI; GigaDB 100295); MOABB Cho2017](http://gigadb.org/dataset/100295) | pretraining | EEG | |
| | [`nm000172`](https://nemar.org/dataset/nm000172) | [High Gamma Dataset (HGD), Schirrmeister et al. 2017 (20 subjects, 128 ch, executed movements); MOABB Schirrmeister2017](https://gin.g-node.org/robintibor/high-gamma-dataset) | pretraining | EEG | |
| | [`nm000139`](https://nemar.org/dataset/nm000139) | [BCI Competition IV dataset 2a (9 subjects, 22 ch, 4-class MI); MOABB BNCI2014_001](https://www.bbci.de/competition/iv/#dataset2a) | pretraining | EEG | |
| | [`nm000135`](https://nemar.org/dataset/nm000135) | [BCI Competition IV dataset 2b (9 subjects, 3 ch, 2-class MI); MOABB BNCI2014_004](https://www.bbci.de/competition/iv/#dataset2b) | pretraining | EEG | |
| | `Kaya2018` | [Kaya et al. 2018, A large electroencephalographic motor imagery dataset for EEG BCIs (Sci. Data 5:180211; Figshare collection 3917698). Paper uses two subsets, 'Large-MI-Classic' and 'Large-MI-5F'. MOABB Kaya2018 (moabb>=1.7.2) loads only the 17 CLA files, not the 5F (or HaLT) files](https://figshare.com/collections/A_large_electroencephalographic_motor_imagery_dataset_for_electroencephalographic_brain_computer_interfaces/3917698) (NEMAR [nm000137](https://nemar.org/dataset/nm000137) holds a subset of it) | pretraining | EEG | |
| | `Won2022` | [Won et al. 2022, EEG dataset for RSVP and P300 speller BCIs (Sci. Data 9:388; 55 participants P300 speller, 50 RSVP; Figshare collection 5769449); not in MOABB](https://doi.org/10.6084/m9.figshare.c.5769449.v1) | pretraining | EEG | |
| | [`nm000129`](https://nemar.org/dataset/nm000129) | [BETA: large benchmark database toward SSVEP-BCI application, Liu et al. 2020 (70 subjects, 40 targets); MOABB Liu2020BETA](https://doi.org/10.3389/fnins.2020.00627) | pretraining | EEG | |
| | [`nm000339`](https://nemar.org/dataset/nm000339) | [Stieger et al. 2021, continuous sensorimotor-rhythm BCI learning in a large population (62 subjects, ~600 h, 62 ch; Figshare 13123148); MOABB Stieger2021](https://doi.org/10.6084/m9.figshare.13123148.v1) | pretraining | EEG | |
| | `SparrKULee` | [SparrKULee: speech-evoked auditory response repository of KU Leuven (Bollens et al. 2023; 85 participants, 64 ch BioSemi); called 'KUL Auditory Decoding Dataset' in the paper](https://doi.org/10.48804/K3VSND) (NEMAR [nm000238](https://nemar.org/dataset/nm000238) holds a subset of it) | pretraining | EEG | |
| | `SEED-V` | [SEED-V emotion EEG dataset (Liu et al. 2022; 16 subjects x 3 sessions, 62 ch, five emotions)](https://bcmi.sjtu.edu.cn/home/seed/seed-v.html) | pretraining | EEG | |
| | `private:KU-Leuven-in-house-EEG` | In-house EEG recorded in the authors' lab (KU Leuven Laboratory for Neuro- & Psychophysiology), multiple paradigms (e.g. MI and SSVEP) and diverse montages; not released, not itemised | pretraining | EEG | |
|
|
| - MAE self-supervised pre-training, 300 epochs (release note). MAE pre-training corpus of App. E.2 (pp. 25-26), '11 public datasets' + an in-house set, as the paper lists them: 1) EEG-MI-BCI (Cho et al., 2017) -> Cho2017; 2) HGD (Schirrmeister et al., 2017) -> Schirrmeister2017; 3) BCI-Comp-IV2a (Tangermann et al., 2012) -> BNCI2014_001 (official training session only; the official test session was the validation split, E.4 p.26); 4) BCI-Comp-IV2b (Tangermann et al., 2012) -> BNCI2014_004; 5) Large-MI-Classic and 6) Large-MI-5F (both Kaya et al., 2018) -> Kaya2018 (one dataset, two paradigm subsets); 7) P300 (Won et al., 2022: P300 speller + RSVP) -> Won2022; 8) SSVEP (Liu et al., 2020 = BETA) -> Liu2020BETA; 9) Online MI BCI Classification (Stieger et al., 2021) -> Stieger2021; 10) KUL Auditory Decoding Dataset (Bollens et al., 2023 = SparrKULee) -> SparrKULee; 11) SEED-V (Liu et al., 2022) -> SEED-V; plus in-house EEG recorded in the authors' lab (multiple paradigms, e.g. MI and SSVEP) -> private:KU-Leuven-in-house-EEG. Preprocessing: notch, 0.1-64 Hz band-pass, 128 Hz, per-channel z-score (E.3). Windows: 6 s / 0.5 s hop for continuous sets, 6 s / 2.5 s hop for the three large MI sets, 2 s / 0.125 s hop for SSVEP (E.4). Validation: BCI-Comp-IV2a official test set; 20% held out for every other set; >8M overlapping segments overall. |
| - Sources: Yang et al., ICLR 2026, https://openreview.net/forum?id=5Xwm8e6vbh; PDF via Wayback snapshot http://web.archive.org/web/20260609032700/https://openreview.net/pdf?id=5Xwm8e6vbh App. E.2, p.25; https://github.com/LiuyinYang1101/STEEGFormer/releases/tag/ST-EEGFormer-small (release body). |
|
|
| ### Scope of this warm-start |
|
|
| This checkpoint inherits the scalp-EEG MAE corpus above from `braindecode/STEEGFormer-small`. No Stanford iEEG training has occurred here. The high-gamma patch embedding is warm-started from the low-frequency embedding; the spatial adapter and task readout are initialized on load and require downstream training. |
|
|
|
|
| ## Usage |
|
|
| ```python |
| from braindecode.models import CORTEG |
| |
| model = CORTEG.from_pretrained( |
| "braindecode/corteg-small-pretrained", n_outputs=5, n_times=128, chs_info=raw.info["chs"] |
| ) |
| ``` |
|
|
| The spatial adapter, the head and, with `merge_strategy="layerwise_gate"`, the |
| gate are not in these files: Braindecode initializes them on load, the adapter |
| from the electrode positions of `chs_info` (metres). The paper then trains the |
| adapter, the high-gamma embedding, LoRA (r=4, alpha=16) on the last four blocks |
| with their LayerNorms, and the head, with the rest frozen. See |
| [`braindecode/corteg-stanford-pooled`](https://huggingface.co/braindecode/corteg-stanford-pooled) |
| for the authors' trained Stanford model and the input streams. |
|
|
| ## Limitations |
|
|
| These files hold no trained intracranial component, so the model needs |
| training before prediction. The check above covers the float32 CPU tensors of |
| the warm start. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{yang2026corteg, |
| title = {{CORTEG}: Foundation Models Enable Cross-Modality Representation Transfer |
| from Scalp to Intracranial Brain Recordings}, |
| author = {Yang, Liuyin and Sun, Qiang and Van Dyck, Bob and Calvo Merino, Eva and |
| Van Hulle, Marc M.}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| The ST-EEGFormer weights are released by Liuyin Yang under the MIT licence |
| (`LICENSE`). Braindecode's code is BSD-3-Clause. |
|
|
|
|
| ## Cite Braindecode |
|
|
| If you use this braindecode port in scientific work, cite the software on Zenodo, |
| [doi:10.5281/zenodo.17699192](https://doi.org/10.5281/zenodo.17699192), |
| and the original model and datasets cited above. |
|
|
| ```bibtex |
| @software{braindecode, |
| author = {Aristimunha, Bruno and |
| Guetschel, Pierre and |
| Wimpff, Martin and |
| Gemein, Lukas and |
| Rommel, Cedric and |
| Banville, Hubert and |
| Sliwowski, Maciej and |
| Wilson, Daniel and |
| Brandt, Simon and |
| Gnassounou, Théo and |
| Paillard, Joseph and |
| {Junqueira Lopes}, Bruna and |
| Sedlar, Sara and |
| Moreau, Thomas and |
| Chevallier, Sylvain and |
| Gramfort, Alexandre and |
| Schirrmeister, Robin Tibor}, |
| title = {Braindecode: toolbox for decoding raw electrophysiological brain data |
| with deep learning models}, |
| url = {https://github.com/braindecode/braindecode}, |
| doi = {10.5281/zenodo.17699192}, |
| publisher = {Zenodo}, |
| license = {BSD-3-Clause}, |
| } |
| ``` |
|
|
