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),
ST-EEGFormer small adapted to intracranial recordings, for
braindecode.models.CORTEG:
the ST-EEGFormer small encoder (checkpoint-300.pth,
sha256 98d2848abdaea7a143d399c07f67f8782e07dd66e7c309ddb058c7158a8a8b02, through its conversion
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,
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.
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
(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:.
- 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
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
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
@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, and the original model and datasets cited above.
@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},
}