Replace with clean markdown card

README.md

@@ -13,13 +13,12 @@ tags:
 
 # EEGSimpleConv
 
-EEGSimpleConv from Ouahidi, YE et al (2023) .
+EEGSimpleConv from Ouahidi, YE et al (2023) [Yassine2023].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.EEGSimpleConv` 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
 
@@ -38,167 +37,41 @@ model = EEGSimpleConv(
 )
 ```
 
-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.EEGSimpleConv.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.EEGSimpleConv.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/eegsimpleconv.py#L21>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>EEGSimpleConv from Ouahidi, YE et al (2023) [Yassine2023]_.</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://raw.githubusercontent.com/elouayas/EEGSimpleConv/refs/heads/main/architecture.png
-     :align: center
-     :alt: EEGSimpleConv Architecture
-
- EEGSimpleConv is a 1D Convolutional Neural Network originally designed
- for decoding motor imagery from EEG signals. The model aims to have a
- very simple and straightforward architecture that allows a low latency,
- while still achieving very competitive performance.
-
- EEG-SimpleConv starts with a 1D convolutional layer, where each EEG channel
- enters a separate 1D convolutional channel. This is followed by a series of
- blocks of two 1D convolutional layers. Between the two convolutional layers
- of each block is a max pooling layer, which downsamples the data by a factor
- of 2. Each convolution is followed by a batch normalisation layer and a ReLU
- activation function. Finally, a global average pooling (in the time domain)
- is performed to obtain a single value per feature map, which is then fed
- into a linear layer to obtain the final classification prediction output.
-
- The paper and original code with more details about the methodological
- choices are available at the [Yassine2023]_ and [Yassine2023Code]_.
-
- The input shape should be three-dimensional matrix representing the EEG
- signals.
-
- ``(batch_size, n_channels, n_timesteps)``.
-
- Notes
- -----
- The authors recommend using the default parameters for MI decoding.
- Please refer to the original paper and code for more details.
-
- Recommended range for the choice of the hyperparameters, regarding the
- evaluation paradigm.
-
- |    Parameter    | Within-Subject | Cross-Subject |
- | feature_maps    | [64-144]       |   [64-144]    |
- | n_convs         |    1           |   [2-4]       |
- | resampling_freq | [70-100]       |   [50-80]     |
- | kernel_size     | [12-17]        |   [5-8]       |
-
-
- An intensive ablation study is included in the paper to understand the
- of each parameter on the model performance.
-
- .. versionadded:: 0.9
-
- Parameters
- ----------
- feature_maps: int
-     Number of Feature Maps at the first Convolution, width of the model.
- n_convs: int
-     Number of blocks of convolutions (2 convolutions per block), depth of the model.
- resampling: int
-     Resampling Frequency.
- kernel_size: int
-     Size of the convolutions kernels.
- activation: nn.Module, default=nn.ELU
-     Activation function class to apply. Should be a PyTorch activation
-     module class like ``nn.ReLU`` or ``nn.ELU``. Default is ``nn.ELU``.
-
- References
- ----------
- .. [Yassine2023] Yassine El Ouahidi, V. Gripon, B. Pasdeloup, G. Bouallegue
-     N. Farrugia, G. Lioi, 2023. A Strong and Simple Deep Learning Baseline for
-     BCI Motor Imagery Decoding. Arxiv preprint. arxiv.org/abs/2309.07159
- .. [Yassine2023Code] Yassine El Ouahidi, V. Gripon, B. Pasdeloup, G. Bouallegue
-     N. Farrugia, G. Lioi, 2023. A Strong and Simple Deep Learning Baseline for
-     BCI Motor Imagery Decoding. GitHub repository.
-     https://github.com/elouayas/EEGSimpleConv.
-
- .. 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 EEGSimpleConv
-
-     # Train your model
-     model = EEGSimpleConv(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-eegsimpleconv-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import EEGSimpleConv
-
-     # Load pretrained model
-     model = EEGSimpleConv.from_pretrained("username/my-eegsimpleconv-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = EEGSimpleConv.from_pretrained("username/my-eegsimpleconv-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"]
+![EEGSimpleConv architecture](https://raw.githubusercontent.com/elouayas/EEGSimpleConv/refs/heads/main/architecture.png)
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## Parameters
 
- .. code::
-     import json
+| Parameter | Type | Description |
+|---|---|---|
+| `feature_maps: int` | — | Number of Feature Maps at the first Convolution, width of the model. |
+| `n_convs: int` | — | Number of blocks of convolutions (2 convolutions per block), depth of the model. |
+| `resampling: int` | — | Resampling Frequency. |
+| `kernel_size: int` | — | Size of the convolutions kernels. |
+| `activation: nn.Module, default=nn.ELU` | — | Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.ELU`. |
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
 
-     model2 = EEGSimpleConv.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. Yassine El Ouahidi, V. Gripon, B. Pasdeloup, G. Bouallegue N. Farrugia, G. Lioi, 2023. A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding. Arxiv preprint. arxiv.org/abs/2309.07159
+2. Yassine El Ouahidi, V. Gripon, B. Pasdeloup, G. Bouallegue N. Farrugia, G. Lioi, 2023. A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding. GitHub repository. https://github.com/elouayas/EEGSimpleConv.
 
- 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,