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Add ST-EEGFormer large (ported from ST-EEGFormer-large)

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  1. README.md +49 -0
  2. config.json +20 -0
  3. model.safetensors +3 -0
  4. pytorch_model.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: braindecode
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+ tags:
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+ - braindecode
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+ - STEEGFormer
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+ - eeg
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+ - foundation-model
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+ license: mit
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+ ---
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+
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+ # STEEGFormer (large)
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+
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+ ViT-MAE EEG foundation model — braindecode port of **ST-EEGFormer** (large variant).
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+
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+ ## Provenance
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+
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+ - **Weights ported from:** [LiuyinYang1101/STEEGFormer](https://github.com/LiuyinYang1101/STEEGFormer),
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+ release [`ST-EEGFormer-large`](https://github.com/LiuyinYang1101/STEEGFormer/releases/tag/ST-EEGFormer-large)
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+ (asset `large_weights_only_196.pth`).
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+ - **Upstream license:** MIT. The braindecode wrapper code is BSD-3-Clause.
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+ - The pre-trained encoder is loaded faithfully (numerical equivalence verified:
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+ pre-encoder bit-exact, post-encoder relative error ~4e-6). The MAE decoder is
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+ dropped and the classification head is re-initialised.
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+
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+ ## Architecture
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+
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+ | | embed_dim | depth | num_heads | patch_size | channel vocab |
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+ |---|---|---|---|---|---|
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+ | large | 1024 | 24 | 16 | 16 | 145 |
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+
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+ Channel positions are resolved from electrode names in `chs_info` (145-slot shared montage vocabulary).
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+
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+ ## Usage
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+
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+ ```python
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+ from braindecode.models import STEEGFormer
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+
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+ model = STEEGFormer.from_pretrained(
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+ "braindecode/STEEGFormer-large",
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+ n_outputs=4, n_chans=22, n_times=1000, chs_info=chs_info,
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+ )
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+ # Encoder features: out = model(x, return_features=True); out["features"]
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+ ```
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+
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+ ## Citation
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+
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+ Yang, L., Sun, Q., Li, A. & Van Hulle, M. M. (2026). *Are EEG foundation models
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+ worth it? Comparative evaluation with traditional decoders in diverse BCI tasks.*
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+ ICLR 2026. https://openreview.net/forum?id=5Xwm8e6vbh
config.json ADDED
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+ {
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+ "n_outputs": 4,
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+ "n_chans": 22,
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+ "chs_info": null,
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+ "n_times": 1000,
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+ "input_window_seconds": null,
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+ "sfreq": null,
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+ "patch_size": 16,
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+ "embed_dim": 1024,
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+ "depth": 24,
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+ "num_heads": 16,
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+ "mlp_ratio": 4.0,
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+ "drop_prob": 0.0,
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+ "drop_path": 0.0,
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+ "activation": "torch.nn.modules.activation.GELU",
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+ "global_pool": "avg",
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+ "n_chans_pos": 145,
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+ "chan_pos_idx": null,
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+ "braindecode_version": "1.6.1dev0"
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+ }
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