Add architecture-only model card

README.md

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+---
+license: bsd-3-clause
+library_name: braindecode
+pipeline_tag: feature-extraction
+tags:
+  - eeg
+  - biosignal
+  - pytorch
+  - neuroscience
+  - braindecode
+  - convolutional
+---
+
+# ShallowFBCSPNet
+
+Shallow ConvNet model from Schirrmeister et al (2017) .
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.ShallowFBCSPNet` class. **No pretrained weights are
+> distributed here** — instantiate the model and train it on your own
+> data, or fine-tune from a published foundation-model checkpoint
+> separately.
+
+## Quick start
+
+```bash
+pip install braindecode
+```
+
+```python
+from braindecode.models import ShallowFBCSPNet
+
+model = ShallowFBCSPNet(
+    n_chans=22,
+    sfreq=250,
+    input_window_seconds=4.0,
+    n_outputs=4,
+)
+```
+
+The signal-shape arguments above are example defaults — adjust them
+to match your recording.
+
+## Documentation
+
+- Full API reference (parameters, references, architecture figure):
+  <https://braindecode.org/stable/generated/braindecode.models.ShallowFBCSPNet.html>
+- Interactive browser with live instantiation:
+  <https://huggingface.co/spaces/braindecode/model-explorer>
+- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/shallow_fbcsp.py#L24>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>Shallow ConvNet model from Schirrmeister et al (2017) [Schirrmeister2017]_.</p>
+<span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#5cb85c;color:white;font-size:11px;font-weight:600;margin-right:4px;">Convolution</span>
+
+
+
+ .. figure:: https://onlinelibrary.wiley.com/cms/asset/221ea375-6701-40d3-ab3f-e411aad62d9e/hbm23730-fig-0002-m.jpg
+     :align: center
+     :alt: ShallowNet Architecture
+
+ Model described in [Schirrmeister2017]_.
+
+ Parameters
+ ----------
+ n_filters_time: int
+     Number of temporal filters.
+ filter_time_length: int
+     Length of the temporal filter.
+ n_filters_spat: int
+     Number of spatial filters.
+ pool_time_length: int
+     Length of temporal pooling filter.
+ pool_time_stride: int
+     Length of stride between temporal pooling filters.
+ final_conv_length: int | str
+     Length of the final convolution layer.
+     If set to "auto", length of the input signal must be specified.
+ conv_nonlin: type[nn.Module] | Callable
+     Non-linear module class to be used after convolution layers.
+     For backward compatibility, callables are also accepted and wrapped
+     with :class:`~braindecode.modules.Expression`.
+ pool_mode: str
+     Method to use on pooling layers. "max" or "mean".
+ activation_pool_nonlin: type[nn.Module]
+     Non-linear module class to be used after pooling layers.
+ split_first_layer: bool
+     Split first layer into temporal and spatial layers (True) or just use temporal (False).
+     There would be no non-linearity between the split layers.
+ batch_norm: bool
+     Whether to use batch normalisation.
+ batch_norm_alpha: float
+     Momentum for BatchNorm2d.
+ drop_prob: float
+     Dropout probability.
+
+ References
+ ----------
+ .. [Schirrmeister2017] Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+    L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
+    & Ball, T. (2017).
+    Deep learning with convolutional neural networks for EEG decoding and
+    visualization.
+    Human Brain Mapping , Aug. 2017.
+    Online: http://dx.doi.org/10.1002/hbm.23730
+
+ .. rubric:: Hugging Face Hub integration
+
+ When the optional ``huggingface_hub`` package is installed, all models
+ automatically gain the ability to be pushed to and loaded from the
+ Hugging Face Hub. Install with::
+
+     pip install braindecode[hub]
+
+ **Pushing a model to the Hub:**
+
+ .. code::
+     from braindecode.models import ShallowFBCSPNet
+
+     # Train your model
+     model = ShallowFBCSPNet(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-shallowfbcspnet-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import ShallowFBCSPNet
+
+     # Load pretrained model
+     model = ShallowFBCSPNet.from_pretrained("username/my-shallowfbcspnet-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = ShallowFBCSPNet.from_pretrained("username/my-shallowfbcspnet-model", n_outputs=4)
+
+ **Extracting features and replacing the head:**
+
+ .. code::
+     import torch
+
+     x = torch.randn(1, model.n_chans, model.n_times)
+     # Extract encoder features (consistent dict across all models)
+     out = model(x, return_features=True)
+     features = out["features"]
+
+     # Replace the classification head
+     model.reset_head(n_outputs=10)
+
+ **Saving and restoring full configuration:**
+
+ .. code::
+     import json
+
+     config = model.get_config()            # all __init__ params
+     with open("config.json", "w") as f:
+         json.dump(config, f)
+
+     model2 = ShallowFBCSPNet.from_config(config)    # reconstruct (no weights)
+
+ All model parameters (both EEG-specific and model-specific such as
+ dropout rates, activation functions, number of filters) are automatically
+ saved to the Hub and restored when loading.
+
+ See :ref:`load-pretrained-models` for a complete tutorial.</main>
+</div>
+
+## Citation
+
+Please cite both the original paper for this architecture (see the
+*References* section above) and braindecode:
+
+```bibtex
+@article{aristimunha2025braindecode,
+  title   = {Braindecode: a deep learning library for raw electrophysiological data},
+  author  = {Aristimunha, Bruno and others},
+  journal = {Zenodo},
+  year    = {2025},
+  doi     = {10.5281/zenodo.17699192},
+}
+```
+
+## License
+
+BSD-3-Clause for the model code (matching braindecode).
+Pretraining-derived weights, if you fine-tune from a checkpoint,
+inherit the licence of that checkpoint and its training corpus.