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
+---
+
+# SincShallowNet
+
+Sinc-ShallowNet from Borra, D et al (2020) .
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.SincShallowNet` 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 SincShallowNet
+
+model = SincShallowNet(
+    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.SincShallowNet.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/sinc_shallow.py#L11>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>Sinc-ShallowNet from Borra, D et al (2020) [borra2020]_.</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><span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#E69F00;color:white;font-size:11px;font-weight:600;margin-right:4px;">Interpretability</span>
+
+
+
+ .. figure:: https://ars.els-cdn.com/content/image/1-s2.0-S0893608020302021-gr2_lrg.jpg
+     :align: center
+     :alt: SincShallowNet Architecture
+
+ The Sinc-ShallowNet architecture has these fundamental blocks:
+
+ 1. **Block 1: Spectral and Spatial Feature Extraction**
+
+     - *Temporal Sinc-Convolutional Layer*: Uses parametrized sinc functions to learn band-pass filters,
+       significantly reducing the number of trainable parameters by only
+       learning the lower and upper cutoff frequencies for each filter.
+     - *Spatial Depthwise Convolutional Layer*: Applies depthwise convolutions to learn spatial filters for
+       each temporal feature map independently, further reducing
+       parameters and enhancing interpretability.
+     - *Batch Normalization*
+
+ 2. **Block 2: Temporal Aggregation**
+
+     - *Activation Function*: ELU
+     - *Average Pooling Layer*: Aggregation by averaging spatial dim
+     - *Dropout Layer*
+     - *Flatten Layer*
+
+ 3. **Block 3: Classification**
+
+     - *Fully Connected Layer*: Maps the feature vector to n_outputs.
+
+ **Implementation Notes:**
+
+ - The sinc-convolutional layer initializes cutoff frequencies uniformly
+     within the desired frequency range and updates them during training while
+     ensuring the lower cutoff is less than the upper cutoff.
+
+ Parameters
+ ----------
+ num_time_filters : int
+     Number of temporal filters in the SincFilter layer.
+ time_filter_len : int
+     Size of the temporal filters.
+ depth_multiplier : int
+     Depth multiplier for spatial filtering.
+ activation : nn.Module, optional
+     Activation function to use. Default is nn.ELU().
+ drop_prob : float, optional
+     Dropout probability. Default is 0.5.
+ first_freq : float, optional
+     The starting frequency for the first Sinc filter. Default is 5.0.
+ min_freq : float, optional
+     Minimum frequency allowed for the low frequencies of the filters. Default is 1.0.
+ freq_stride : float, optional
+     Frequency stride for the Sinc filters. Controls the spacing between the filter frequencies.
+     Default is 1.0.
+ padding : str, optional
+     Padding mode for convolution, either 'same' or 'valid'. Default is 'same'.
+ bandwidth : float, optional
+     Initial bandwidth for each Sinc filter. Default is 4.0.
+ pool_size : int, optional
+     Size of the pooling window for the average pooling layer. Default is 55.
+ pool_stride : int, optional
+     Stride of the pooling operation. Default is 12.
+
+ Notes
+ -----
+ This implementation is based on the implementation from [sincshallowcode]_.
+
+ References
+ ----------
+ .. [borra2020] Borra, D., Fantozzi, S., & Magosso, E. (2020). Interpretable
+    and lightweight convolutional neural network for EEG decoding: Application
+    to movement execution and imagination. Neural Networks, 129, 55-74.
+ .. [sincshallowcode] Sinc-ShallowNet re-implementation source code:
+    https://github.com/marcellosicbaldi/SincNet-Tensorflow
+
+ .. 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 SincShallowNet
+
+     # Train your model
+     model = SincShallowNet(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-sincshallownet-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import SincShallowNet
+
+     # Load pretrained model
+     model = SincShallowNet.from_pretrained("username/my-sincshallownet-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = SincShallowNet.from_pretrained("username/my-sincshallownet-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 = SincShallowNet.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.