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
+---
+
+# EEGInceptionMI
+
+EEG Inception for Motor Imagery, as proposed in Zhang et al. (2021)
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.EEGInceptionMI` 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 EEGInceptionMI
+
+model = EEGInceptionMI(
+    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.EEGInceptionMI.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/eeginception_mi.py#L14>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>EEG Inception for Motor Imagery, as proposed in Zhang et al. (2021) [1]_</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://content.cld.iop.org/journals/1741-2552/18/4/046014/revision3/jneabed81f1_hr.jpg
+     :align: center
+     :alt: EEGInceptionMI Architecture
+
+
+ The model is strongly based on the original InceptionNet for computer
+ vision. The main goal is to extract features in parallel with different
+ scales. The network has two blocks made of 3 inception modules with a skip
+ connection.
+
+ The model is fully described in [1]_.
+
+ Notes
+ -----
+ This implementation is not guaranteed to be correct, has not been checked
+ by original authors, only reimplemented bosed on the paper [1]_.
+
+ Parameters
+ ----------
+ input_window_seconds : float, optional
+     Size of the input, in seconds. Set to 4.5 s as in [1]_ for dataset
+     BCI IV 2a.
+ sfreq : float, optional
+     EEG sampling frequency in Hz. Defaults to 250 Hz as in [1]_ for dataset
+     BCI IV 2a.
+ n_convs : int, optional
+     Number of convolution per inception wide branching. Defaults to 5 as
+     in [1]_ for dataset BCI IV 2a.
+ n_filters : int, optional
+     Number of convolutional filters for all layers of this type. Set to 48
+     as in [1]_ for dataset BCI IV 2a.
+ kernel_unit_s : float, optional
+     Size in seconds of the basic 1D convolutional kernel used in inception
+     modules. Each convolutional layer in such modules have kernels of
+     increasing size, odd multiples of this value (e.g. 0.1, 0.3, 0.5, 0.7,
+     0.9 here for ``n_convs=5``). Defaults to 0.1 s.
+ activation: nn.Module
+     Activation function. Defaults to ReLU activation.
+
+ References
+ ----------
+ .. [1] Zhang, C., Kim, Y. K., & Eskandarian, A. (2021).
+     EEG-inception: an accurate and robust end-to-end neural network
+     for EEG-based motor imagery classification.
+     Journal of Neural Engineering, 18(4), 046014.
+
+ .. 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 EEGInceptionMI
+
+     # Train your model
+     model = EEGInceptionMI(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-eeginceptionmi-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import EEGInceptionMI
+
+     # Load pretrained model
+     model = EEGInceptionMI.from_pretrained("username/my-eeginceptionmi-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = EEGInceptionMI.from_pretrained("username/my-eeginceptionmi-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 = EEGInceptionMI.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.