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
+
+---
+
+# SyncNet
+
+Synchronization Network (SyncNet) from Li, Y et al (2017) .
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.SyncNet` 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 SyncNet
+
+model = SyncNet(
+    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.SyncNet.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/syncnet.py#L14>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>Synchronization Network (SyncNet) from Li, Y et al (2017) [Li2017]_.</p>
+<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://braindecode.org/dev/_static/model/SyncNet.png
+     :align: center
+     :alt: SyncNet Architecture
+
+ SyncNet uses parameterized 1-dimensional convolutional filters inspired by
+ the Morlet wavelet to extract features from EEG signals. The filters are
+ dynamically generated based on learnable parameters that control the
+ oscillation and decay characteristics.
+
+ The filter for channel ``c`` and filter ``k`` is defined as:
+
+ .. math::
+
+     f_c^{(k)}(\\tau) = amplitude_c^{(k)} \\cos(\\omega^{(k)} \\tau + \\phi_c^{(k)}) \\exp(-\\beta^{(k)} \\tau^2)
+
+ where:
+ - :math:`amplitude_c^{(k)}` is the amplitude parameter (channel-specific).
+ - :math:`\\omega^{(k)}` is the frequency parameter (shared across channels).
+ - :math:`\\phi_c^{(k)}` is the phase shift (channel-specific).
+ - :math:`\\beta^{(k)}` is the decay parameter (shared across channels).
+ - :math:`\\tau` is the time index.
+
+ Parameters
+ ----------
+ num_filters : int, optional
+     Number of filters in the convolutional layer. Default is 1.
+ filter_width : int, optional
+     Width of the convolutional filters. Default is 40.
+ pool_size : int, optional
+     Size of the pooling window. Default is 40.
+ activation : nn.Module, optional
+     Activation function to apply after pooling. Default is ``nn.ReLU``.
+ ampli_init_values : tuple of float, optional
+     The initialization range for amplitude parameter using uniform
+     distribution. Default is (-0.05, 0.05).
+ omega_init_values : tuple of float, optional
+     The initialization range for omega parameters using uniform
+     distribution. Default is (0, 1).
+ beta_init_values : tuple of float, optional
+     The initialization range for beta (decay) parameters using uniform
+     distribution. Default is (0, 0.05).
+ phase_init_values : tuple of float, optional
+     The initialization mean and standard deviation for phase
+     parameters using normal distribution. Default is (0, 0.05).
+
+
+ Notes
+ -----
+ This implementation is not guaranteed to be correct! it has not been checked
+ by original authors. The modifications are based on derivated code from
+ [CodeICASSP2025]_.
+
+
+ References
+ ----------
+ .. [Li2017] Li, Y., Dzirasa, K., Carin, L., & Carlson, D. E. (2017).
+    Targeting EEG/LFP synchrony with neural nets. Advances in neural
+    information processing systems, 30.
+ .. [CodeICASSP2025] Code from Baselines for EEG-Music Emotion Recognition
+    Grand Challenge at ICASSP 2025.
+    https://github.com/SalvoCalcagno/eeg-music-challenge-icassp-2025-baselines
+
+ .. 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 SyncNet
+
+     # Train your model
+     model = SyncNet(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-syncnet-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import SyncNet
+
+     # Load pretrained model
+     model = SyncNet.from_pretrained("username/my-syncnet-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = SyncNet.from_pretrained("username/my-syncnet-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 = SyncNet.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.