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braindecode/STEEGFormer-small

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Model Card

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).

Usage

python

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