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README.md

@@ -13,13 +13,12 @@ tags:
 
 # SincShallowNet
 
-Sinc-ShallowNet from Borra, D et al (2020) .
+Sinc-ShallowNet from Borra, D et al (2020) [borra2020].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** 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.
+> distributed here.** Instantiate the model and train it on your own
+> data.
 
 ## Quick start
 
@@ -38,170 +37,48 @@ model = SincShallowNet(
 )
 ```
 
-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.SincShallowNet.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.SincShallowNet.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/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:**
+## Architecture
 
- .. code::
-     from braindecode.models import SincShallowNet
+![SincShallowNet architecture](https://ars.els-cdn.com/content/image/1-s2.0-S0893608020302021-gr2_lrg.jpg)
 
-     # 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
+## Parameters
 
-     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"]
+| Parameter | Type | Description |
+|---|---|---|
+| `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. |
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## References
 
- .. code::
-     import json
+1. 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.
+2. Sinc-ShallowNet re-implementation source code: https://github.com/marcellosicbaldi/SincNet-Tensorflow
 
-     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:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,