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README.md

@@ -13,13 +13,12 @@ tags:
 
 # ShallowFBCSPNet
 
-Shallow ConvNet model from Schirrmeister et al (2017) .
+Shallow ConvNet model from Schirrmeister et al (2017) [Schirrmeister2017].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** 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.
+> distributed here.** Instantiate the model and train it on your own
+> data.
 
 ## Quick start
 
@@ -38,146 +37,48 @@ model = ShallowFBCSPNet(
 )
 ```
 
-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.ShallowFBCSPNet.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.ShallowFBCSPNet.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/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 ...
+## Architecture
 
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-shallowfbcspnet-model",
-         commit_message="Initial model upload",
-     )
+![ShallowFBCSPNet architecture](https://onlinelibrary.wiley.com/cms/asset/221ea375-6701-40d3-ab3f-e411aad62d9e/hbm23730-fig-0002-m.jpg)
 
- **Loading a model from the Hub:**
 
- .. code::
-     from braindecode.models import ShallowFBCSPNet
+## Parameters
 
-     # Load pretrained model
-     model = ShallowFBCSPNet.from_pretrained("username/my-shallowfbcspnet-model")
+| Parameter | Type | Description |
+|---|---|---|
+| `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. |
 
-     # 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:**
+## References
 
- .. code::
-     import torch
+1. 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
 
-     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:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,