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README.md

@@ -13,13 +13,12 @@ tags:
 
 # EEGTCNet
 
-EEGTCNet model from Ingolfsson et al (2020) .
+EEGTCNet model from Ingolfsson et al (2020) [ingolfsson2020].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.EEGTCNet` 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
 
@@ -38,132 +37,44 @@ model = EEGTCNet(
 )
 ```
 
-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.EEGTCNet.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.EEGTCNet.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/eegtcnet.py#L15>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>EEGTCNet model from Ingolfsson et al (2020) [ingolfsson2020]_.</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://braindecode.org/dev/_static/model/eegtcnet.jpg
-     :align: center
-     :alt: EEGTCNet Architecture
-
- Combining EEGNet and TCN blocks.
-
- Parameters
- ----------
- activation : nn.Module, optional
-     Activation function to use. Default is `nn.ELU()`.
- depth_multiplier : int, optional
-     Depth multiplier for the depthwise convolution. Default is 2.
- filter_1 : int, optional
-     Number of temporal filters in the first convolutional layer. Default is 8.
- kern_length : int, optional
-     Length of the temporal kernel in the first convolutional layer. Default is 64.
- dropout : float, optional
-     Dropout rate. Default is 0.5.
- depth : int, optional
-     Number of residual blocks in the TCN. Default is 2.
- kernel_size : int, optional
-     Size of the temporal convolutional kernel in the TCN. Default is 4.
- filters : int, optional
-     Number of filters in the TCN convolutional layers. Default is 12.
- max_norm_const : float
-     Maximum L2-norm constraint imposed on weights of the last
-     fully-connected layer. Defaults to 0.25.
-
- References
- ----------
- .. [ingolfsson2020] Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N.,
-     Cavigelli, L., & Benini, L. (2020). EEG-TCNet: An accurate temporal
-     convolutional network for embedded motor-imagery brain–machine interfaces.
-     https://doi.org/10.48550/arXiv.2006.00622
-
- .. 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 EEGTCNet
-
-     # Train your model
-     model = EEGTCNet(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-eegtcnet-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import EEGTCNet
-
-     # Load pretrained model
-     model = EEGTCNet.from_pretrained("username/my-eegtcnet-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = EEGTCNet.from_pretrained("username/my-eegtcnet-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"]
+![EEGTCNet architecture](https://braindecode.org/dev/_static/model/eegtcnet.jpg)
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## Parameters
 
- .. code::
-     import json
+| Parameter | Type | Description |
+|---|---|---|
+| `activation` | nn.Module, optional | Activation function to use. Default is `nn.ELU()`. |
+| `depth_multiplier` | int, optional | Depth multiplier for the depthwise convolution. Default is 2. |
+| `filter_1` | int, optional | Number of temporal filters in the first convolutional layer. Default is 8. |
+| `kern_length` | int, optional | Length of the temporal kernel in the first convolutional layer. Default is 64. |
+| `dropout` | float, optional | Dropout rate. Default is 0.5. |
+| `depth` | int, optional | Number of residual blocks in the TCN. Default is 2. |
+| `kernel_size` | int, optional | Size of the temporal convolutional kernel in the TCN. Default is 4. |
+| `filters` | int, optional | Number of filters in the TCN convolutional layers. Default is 12. |
+| `max_norm_const` | float | Maximum L2-norm constraint imposed on weights of the last fully-connected layer. Defaults to 0.25. |
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
 
-     model2 = EEGTCNet.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. Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., & Benini, L. (2020). EEG-TCNet: An accurate temporal convolutional network for embedded motor-imagery brain–machine interfaces. https://doi.org/10.48550/arXiv.2006.00622
 
- 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,