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README.md

@@ -13,13 +13,12 @@ tags:
 
 # SyncNet
 
-Synchronization Network (SyncNet) from Li, Y et al (2017) .
+Synchronization Network (SyncNet) from Li, Y et al (2017) [Li2017].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** 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.
+> distributed here.** Instantiate the model and train it on your own
+> data.
 
 ## Quick start
 
@@ -38,159 +37,44 @@ model = SyncNet(
 )
 ```
 
-The signal-shape arguments above are example defaults — adjust them
-to match your recording.
+The signal-shape arguments above are illustrative defaults — adjust 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:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.SyncNet.html>
+- Interactive browser (live instantiation, parameter counts):
   <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
+## Architecture
 
-     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"]
+![SyncNet architecture](https://braindecode.org/dev/_static/model/SyncNet.png)
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## Parameters
 
- .. code::
-     import json
+| Parameter | Type | Description |
+|---|---|---|
+| `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). |
 
-     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)
+## References
 
- 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.
+1. Li, Y., Dzirasa, K., Carin, L., & Carlson, D. E. (2017). Targeting EEG/LFP synchrony with neural nets. Advances in neural information processing systems, 30.
+2. Code from Baselines for EEG-Music Emotion Recognition Grand Challenge at ICASSP 2025. https://github.com/SalvoCalcagno/eeg-music-challenge-icassp-2025-baselines
 
- 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:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,