README.md

metadata

library_name: braindecode
tags:
  - braindecode
  - STEEGFormer
  - eeg
  - foundation-model
license: mit

STEEGFormer (base)

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

Provenance

• Weights ported from: LiuyinYang1101/STEEGFormer, release ST-EEGFormer-base (asset checkpoint-288.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
base	768	12	12	16	145

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

Usage

from braindecode.models import STEEGFormer

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