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README.md

@@ -14,13 +14,12 @@ tags:
 
 # SleepStagerChambon2018
 
-Sleep staging architecture from Chambon et al. (2018) .
+Sleep staging architecture from Chambon et al. (2018) [Chambon2018].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.SleepStagerChambon2018` 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
 
@@ -39,143 +38,46 @@ model = SleepStagerChambon2018(
 )
 ```
 
-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.SleepStagerChambon2018.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.SleepStagerChambon2018.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/sleep_stager_chambon_2018.py#L13>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>Sleep staging architecture from Chambon et al. (2018) [Chambon2018]_.</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://braindecode.org/dev/_static/model/SleepStagerChambon2018.jpg
-     :align: center
-     :alt: SleepStagerChambon2018 Architecture
-
- Convolutional neural network for sleep staging described in [Chambon2018]_.
-
- Parameters
- ----------
- n_conv_chs : int
-     Number of convolutional channels. Set to 8 in [Chambon2018]_.
- time_conv_size_s : float
-     Size of filters in temporal convolution layers, in seconds. Set to 0.5
-     in [Chambon2018]_ (64 samples at sfreq=128).
- max_pool_size_s : float
-     Max pooling size, in seconds. Set to 0.125 in [Chambon2018]_ (16
-     samples at sfreq=128).
- pad_size_s : float
-     Padding size, in seconds. Set to 0.25 in [Chambon2018]_ (half the
-     temporal convolution kernel size).
- drop_prob : float
-     Dropout rate before the output dense layer.
- apply_batch_norm : bool
-     If True, apply batch normalization after both temporal convolutional
-     layers.
- return_feats : bool
-     If True, return the features, i.e. the output of the feature extractor
-     (before the final linear layer). If False, pass the features through
-     the final linear layer.
- n_channels : int
-     Alias for `n_chans`.
- input_size_s:
-     Alias for `input_window_seconds`.
- n_classes:
-     Alias for `n_outputs`.
- activation: nn.Module, default=nn.ReLU
-     Activation function class to apply. Should be a PyTorch activation
-     module class like ``nn.ReLU`` or ``nn.ELU``. Default is ``nn.ReLU``.
-
- References
- ----------
- .. [Chambon2018] Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., &
-        Gramfort, A. (2018). A deep learning architecture for temporal sleep
-        stage classification using multivariate and multimodal time series.
-        IEEE Transactions on Neural Systems and Rehabilitation Engineering,
-        26(4), 758-769.
-
- .. 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 SleepStagerChambon2018
-
-     # Train your model
-     model = SleepStagerChambon2018(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-sleepstagerchambon2018-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
 
- .. code::
-     from braindecode.models import SleepStagerChambon2018
+## Architecture
 
-     # Load pretrained model
-     model = SleepStagerChambon2018.from_pretrained("username/my-sleepstagerchambon2018-model")
+![SleepStagerChambon2018 architecture](https://braindecode.org/dev/_static/model/SleepStagerChambon2018.jpg)
 
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = SleepStagerChambon2018.from_pretrained("username/my-sleepstagerchambon2018-model", n_outputs=4)
 
- **Extracting features and replacing the head:**
+## Parameters
 
- .. code::
-     import torch
+| Parameter | Type | Description |
+|---|---|---|
+| `n_conv_chs` | int | Number of convolutional channels. Set to 8 in [Chambon2018]. |
+| `time_conv_size_s` | float | Size of filters in temporal convolution layers, in seconds. Set to 0.5 in [Chambon2018] (64 samples at sfreq=128). |
+| `max_pool_size_s` | float | Max pooling size, in seconds. Set to 0.125 in [Chambon2018] (16 samples at sfreq=128). |
+| `pad_size_s` | float | Padding size, in seconds. Set to 0.25 in [Chambon2018] (half the temporal convolution kernel size). |
+| `drop_prob` | float | Dropout rate before the output dense layer. |
+| `apply_batch_norm` | bool | If True, apply batch normalization after both temporal convolutional layers. |
+| `return_feats` | bool | If True, return the features, i.e. the output of the feature extractor (before the final linear layer). If False, pass the features through the final linear layer. |
+| `n_channels` | int | Alias for `n_chans`. |
+| `input_size_s:` | — | Alias for `input_window_seconds`. |
+| `n_classes:` | — | Alias for `n_outputs`. |
+| `activation: nn.Module, default=nn.ReLU` | — | Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.ReLU`. |
 
-     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)
+## References
 
- **Saving and restoring full configuration:**
-
- .. code::
-     import json
+1. Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., & Gramfort, A. (2018). A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(4), 758-769.
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
-
-     model2 = SleepStagerChambon2018.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,