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
+  - transformer
+---
+
+# MSVTNet
+
+MSVTNet model from Liu K et al (2024) from .
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.MSVTNet` 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 MSVTNet
+
+model = MSVTNet(
+    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.MSVTNet.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/msvtnet.py#L13>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>MSVTNet model from Liu K et al (2024) from [msvt2024]_.</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><span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#56B4E9;color:white;font-size:11px;font-weight:600;margin-right:4px;">Attention/Transformer</span>
+
+
+
+ This model implements a multi-scale convolutional transformer network
+ for EEG signal classification, as described in [msvt2024]_.
+
+ .. figure:: https://raw.githubusercontent.com/SheepTAO/MSVTNet/refs/heads/main/MSVTNet_Arch.png
+    :align: center
+    :alt: MSVTNet Architecture
+
+ Parameters
+ ----------
+ n_filters_list : list[int], optional
+     List of filter numbers for each TSConv block, by default (9, 9, 9, 9).
+ conv1_kernels_size : list[int], optional
+     List of kernel sizes for the first convolution in each TSConv block,
+     by default (15, 31, 63, 125).
+ conv2_kernel_size : int, optional
+     Kernel size for the second convolution in TSConv blocks, by default 15.
+ depth_multiplier : int, optional
+     Depth multiplier for depthwise convolution, by default 2.
+ pool1_size : int, optional
+     Pooling size for the first pooling layer in TSConv blocks, by default 8.
+ pool2_size : int, optional
+     Pooling size for the second pooling layer in TSConv blocks, by default 7.
+ drop_prob : float, optional
+     Dropout probability for convolutional layers, by default 0.3.
+ num_heads : int, optional
+     Number of attention heads in the transformer encoder, by default 8.
+ ffn_expansion_factor : float, optional
+     Ratio to compute feedforward dimension in the transformer, by default 1.
+ att_drop_prob : float, optional
+     Dropout probability for the transformer, by default 0.5.
+ num_layers : int, optional
+     Number of transformer encoder layers, by default 2.
+ activation : Type[nn.Module], optional
+     Activation function class to use, by default nn.ELU.
+ return_features : bool, optional
+     Whether to return predictions from branch classifiers, by default False.
+
+ Notes
+ -----
+ This implementation is not guaranteed to be correct, has not been checked
+ by original authors, only reimplemented based on the original code [msvt2024code]_.
+
+ References
+ ----------
+ .. [msvt2024] Liu, K., et al. (2024). MSVTNet: Multi-Scale Vision
+    Transformer Neural Network for EEG-Based Motor Imagery Decoding.
+    IEEE Journal of Biomedical an Health Informatics.
+ .. [msvt2024code] Liu, K., et al. (2024). MSVTNet: Multi-Scale Vision
+    Transformer Neural Network for EEG-Based Motor Imagery Decoding.
+    Source Code: https://github.com/SheepTAO/MSVTNet
+
+ .. 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 MSVTNet
+
+     # Train your model
+     model = MSVTNet(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-msvtnet-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import MSVTNet
+
+     # Load pretrained model
+     model = MSVTNet.from_pretrained("username/my-msvtnet-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = MSVTNet.from_pretrained("username/my-msvtnet-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 = MSVTNet.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.