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
+
+---
+
+# EEGNetv4
+
+Deprecated alias for EEGNet.
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.EEGNetv4` 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 EEGNetv4
+
+model = EEGNetv4(
+    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.EEGNetv4.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/eegnet.py#L353>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>Deprecated alias for EEGNet.</p>
+<blockquote>
+<p class="rubric">Hugging Face Hub integration</p>
+<p>When the optional <span class="docutils literal">huggingface_hub</span> package is installed, all models
+automatically gain the ability to be pushed to and loaded from the
+Hugging Face Hub. Install with:</p>
+<pre class="literal-block">pip install braindecode[hub]</pre>
+<p><strong>Pushing a model to the Hub:</strong></p>
+<p><strong>Loading a model from the Hub:</strong></p>
+<p><strong>Extracting features and replacing the head:</strong></p>
+<p><strong>Saving and restoring full configuration:</strong></p>
+<p>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.</p>
+<p>See :ref:`load-pretrained-models` for a complete tutorial.</p>
+</blockquote>
+</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.