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
+---
+
+# CTNet
+
+CTNet from Zhao, W et al (2024) .
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.CTNet` 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 CTNet
+
+model = CTNet(
+    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.CTNet.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/ctnet.py#L27>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>CTNet from Zhao, W et al (2024) [ctnet]_.</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:#56B4E9;color:white;font-size:11px;font-weight:600;margin-right:4px;">Attention/Transformer</span>
+
+
+
+  A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
+
+  .. figure:: https://raw.githubusercontent.com/snailpt/CTNet/main/architecture.png
+     :align: center
+     :alt: CTNet Architecture
+
+ CTNet is an end-to-end neural network architecture designed for classifying motor imagery (MI) tasks from EEG signals.
+ The model combines convolutional neural networks (CNNs) with a Transformer encoder to capture both local and global temporal dependencies in the EEG data.
+
+ The architecture consists of three main components:
+
+ 1. **Convolutional Module**:
+
+     - Apply :class:`EEGNet` to perform some feature extraction, denoted here as
+       _PatchEmbeddingEEGNet module.
+
+ 2. **Transformer Encoder Module**:
+
+     - Utilizes multi-head self-attention mechanisms as EEGConformer but
+       with residual blocks.
+
+ 3. **Classifier Module**:
+
+     - Combines features from both the convolutional module
+       and the Transformer encoder.
+     - Flattens the combined features and applies dropout for regularization.
+     - Uses a fully connected layer to produce the final classification output.
+
+ Parameters
+ ----------
+ activation : nn.Module, default=nn.GELU
+     Activation function to use in the network.
+ num_heads : int, default=4
+     Number of attention heads in the Transformer encoder.
+ embed_dim : int or None, default=None
+     Embedding size (dimensionality) for the Transformer encoder.
+ num_layers : int, default=6
+     Number of encoder layers in the Transformer.
+ n_filters_time : int, default=20
+     Number of temporal filters in the first convolutional layer.
+ kernel_size : int, default=64
+     Kernel size for the temporal convolutional layer.
+ depth_multiplier : int, default=2
+     Multiplier for the number of depth-wise convolutional filters.
+ pool_size_1 : int, default=8
+     Pooling size for the first average pooling layer.
+ pool_size_2 : int, default=8
+     Pooling size for the second average pooling layer.
+     cnn_drop_prob: float, default=0.3
+     Dropout probability after convolutional layers.
+ att_positional_drop_prob : float, default=0.1
+     Dropout probability for the positional encoding in the Transformer.
+ final_drop_prob : float, default=0.5
+     Dropout probability before the final classification layer.
+
+ Notes
+ -----
+ This implementation is adapted from the original CTNet source code
+ [ctnetcode]_ to comply with Braindecode's model standards.
+
+ References
+ ----------
+ .. [ctnet] Zhao, W., Jiang, X., Zhang, B., Xiao, S., & Weng, S. (2024).
+     CTNet: a convolutional transformer network for EEG-based motor imagery
+     classification. Scientific Reports, 14(1), 20237.
+ .. [ctnetcode] Zhao, W., Jiang, X., Zhang, B., Xiao, S., & Weng, S. (2024).
+     CTNet source code:
+     https://github.com/snailpt/CTNet
+
+ .. 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 CTNet
+
+     # Train your model
+     model = CTNet(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-ctnet-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import CTNet
+
+     # Load pretrained model
+     model = CTNet.from_pretrained("username/my-ctnet-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = CTNet.from_pretrained("username/my-ctnet-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 = CTNet.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.