Add architecture-only model card

README.md

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+---
+license: bsd-3-clause
+library_name: braindecode
+pipeline_tag: feature-extraction
+tags:
+  - eeg
+  - biosignal
+  - pytorch
+  - neuroscience
+  - braindecode
+  - convolutional
+---
+
+# EEGSimpleConv
+
+EEGSimpleConv from Ouahidi, YE et al (2023) .
+
+> **Architecture-only repository.** This repo 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.
+
+## Quick start
+
+```bash
+pip install braindecode
+```
+
+```python
+from braindecode.models import EEGSimpleConv
+
+model = EEGSimpleConv(
+    n_chans=22,
+    sfreq=250,
+    input_window_seconds=4.0,
+    n_outputs=4,
+)
+```
+
+The signal-shape arguments above are example defaults — adjust them
+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:
+  <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
+
+     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 = EEGSimpleConv.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:
+
+```bibtex
+@article{aristimunha2025braindecode,
+  title   = {Braindecode: a deep learning library for raw electrophysiological data},
+  author  = {Aristimunha, Bruno and others},
+  journal = {Zenodo},
+  year    = {2025},
+  doi     = {10.5281/zenodo.17699192},
+}
+```
+
+## License
+
+BSD-3-Clause for the model code (matching braindecode).
+Pretraining-derived weights, if you fine-tune from a checkpoint,
+inherit the licence of that checkpoint and its training corpus.