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README.md

@@ -13,13 +13,12 @@ tags:
 
 # BDTCN
 
-Braindecode TCN from Gemein, L et al (2020) .
+Braindecode TCN from Gemein, L et al (2020) [gemein2020].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.BDTCN` 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
 
@@ -38,123 +37,40 @@ model = BDTCN(
 )
 ```
 
-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.BDTCN.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.BDTCN.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/tcn.py#L14>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>Braindecode TCN from Gemein, L et al (2020) [gemein2020]_.</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:#6c757d;color:white;font-size:11px;font-weight:600;margin-right:4px;">Recurrent</span>
-
-
-
- .. figure:: https://ars.els-cdn.com/content/image/1-s2.0-S1053811920305073-gr3_lrg.jpg
-    :align: center
-    :alt: Braindecode TCN Architecture
-
- See [gemein2020]_ for details.
-
- Parameters
- ----------
- n_filters: int
-     number of output filters of each convolution
- n_blocks: int
-     number of temporal blocks in the network
- kernel_size: int
-     kernel size of the convolutions
- drop_prob: float
-     dropout probability
- 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
- ----------
- .. [gemein2020] Gemein, L. A., Schirrmeister, R. T., Chrabąszcz, P., Wilson, D.,
-    Boedecker, J., Schulze-Bonhage, A., ... & Ball, T. (2020). Machine-learning-based
-    diagnostics of EEG pathology. NeuroImage, 220, 117021.
-
- .. 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 BDTCN
-
-     # Train your model
-     model = BDTCN(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-bdtcn-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import BDTCN
-
-     # Load pretrained model
-     model = BDTCN.from_pretrained("username/my-bdtcn-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = BDTCN.from_pretrained("username/my-bdtcn-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"]
+![BDTCN architecture](https://ars.els-cdn.com/content/image/1-s2.0-S1053811920305073-gr3_lrg.jpg)
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## Parameters
 
- .. code::
-     import json
+| Parameter | Type | Description |
+|---|---|---|
+| `n_filters: int` | — | number of output filters of each convolution |
+| `n_blocks: int` | — | number of temporal blocks in the network |
+| `kernel_size: int` | — | kernel size of the convolutions |
+| `drop_prob: float` | — | dropout probability |
+| `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`. |
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
 
-     model2 = BDTCN.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. Gemein, L. A., Schirrmeister, R. T., Chrabąszcz, P., Wilson, D., Boedecker, J., Schulze-Bonhage, A., ... & Ball, T. (2020). Machine-learning-based diagnostics of EEG pathology. NeuroImage, 220, 117021.
 
- 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,