braindecode/STEEGFormer-small

STEEGFormer (small)

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

Provenance

• Weights ported from: LiuyinYang1101/STEEGFormer, release ST-EEGFormer-small (asset checkpoint-300.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
small	512	8	8	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, 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).

Usage

from braindecode.models import STEEGFormer

model = STEEGFormer.from_pretrained(
    "braindecode/STEEGFormer-small",
    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