STEEGFormer (largeV2)
ViT-MAE EEG foundation model — braindecode port of ST-EEGFormer (largeV2 variant).
Provenance
- Weights ported from: LiuyinYang1101/STEEGFormer,
release
ST-EEGFormer-largeV2(assetlarge_weights_only_210.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 | |
|---|---|---|---|---|---|
| largeV2 | 1024 | 24 | 16 | 16 | 256 |
This variant uses a 256-slot channel vocabulary (further pre-trained on HBN for the EEG 2025 Foundation Challenge). Name-based mapping via chs_info works for standard electrodes; pass chan_pos_idx explicitly for the HBN montage / non-standard channels.
Usage
from braindecode.models import STEEGFormer
model = STEEGFormer.from_pretrained(
"braindecode/STEEGFormer-largeV2",
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