braindecode/STEEGFormer-large

STEEGFormer (large)

ViT-MAE EEG foundation model — braindecode port of ST-EEGFormer (large variant).

Provenance

• Weights ported from: LiuyinYang1101/STEEGFormer, release ST-EEGFormer-large (asset large_weights_only_196.pth).
• Upstream license: MIT. The braindecode wrapper code is BSD-3-Clause.
• The pre-trained encoder is loaded faithfully (numerical equivalence verified: pre-encoder bit-exact, post-encoder relative error ~4e-6). The MAE decoder is dropped and the classification head is re-initialised.

Architecture

	embed_dim	depth	num_heads	patch_size	channel vocab
large	1024	24	16	16	145

Channel positions are resolved from electrode names in chs_info (145-slot shared montage vocabulary).

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:.

datasets: id	Dataset	Stage	Modality
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	pretraining	EEG
nm000172	High Gamma Dataset (HGD), Schirrmeister et al. 2017 (20 subjects, 128 ch, executed movements); MOABB Schirrmeister2017	pretraining	EEG
nm000139	BCI Competition IV dataset 2a (9 subjects, 22 ch, 4-class MI); MOABB BNCI2014_001	pretraining	EEG
nm000135	BCI Competition IV dataset 2b (9 subjects, 3 ch, 2-class MI); MOABB BNCI2014_004	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 (NEMAR 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	pretraining	EEG
nm000129	BETA: large benchmark database toward SSVEP-BCI application, Liu et al. 2020 (70 subjects, 40 targets); MOABB Liu2020BETA	pretraining	EEG
nm000339	Stieger et al. 2021, continuous sensorimotor-rhythm BCI learning in a large population (62 subjects, ~600 h, 62 ch; Figshare 13123148); MOABB Stieger2021	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 (NEMAR 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)	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, 196 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-large (release body).

Usage

from braindecode.models import STEEGFormer

model = STEEGFormer.from_pretrained(
    "braindecode/STEEGFormer-large",
    n_outputs=4, n_chans=22, n_times=1000, chs_info=chs_info,
)
# Encoder features:  out = model(x, return_features=True); out["features"]

Citation

Yang, L., Sun, Q., Li, A. & Van Hulle, M. M. (2026). Are EEG foundation models worth it? Comparative evaluation with traditional decoders in diverse BCI tasks. ICLR 2026. https://openreview.net/forum?id=5Xwm8e6vbh

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},
}