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
+---
+
+# TSception
+
+TSception model from Ding et al. (2020) from .
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.TSception` 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 TSception
+
+model = TSception(
+    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.TSception.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/tsinception.py#L15>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>TSception model from Ding et al. (2020) from [ding2020]_.</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>
+
+
+
+ TSception: A deep learning framework for emotion detection using EEG.
+
+ .. figure:: https://user-images.githubusercontent.com/58539144/74716976-80415e00-526a-11ea-9433-02ab2b753f6b.PNG
+     :align: center
+     :alt: TSception Architecture
+
+ The model consists of temporal and spatial convolutional layers
+ (Tception and Sception) designed to learn temporal and spatial features
+ from EEG data.
+
+ Parameters
+ ----------
+ number_filter_temp : int
+     Number of temporal convolutional filters.
+ number_filter_spat : int
+     Number of spatial convolutional filters.
+ hidden_size : int
+     Number of units in the hidden fully connected layer.
+ drop_prob : float
+     Dropout rate applied after the hidden layer.
+ activation : nn.Module, optional
+     Activation function class to apply. Should be a PyTorch activation
+     module like ``nn.ReLU`` or ``nn.LeakyReLU``. Default is ``nn.LeakyReLU``.
+ pool_size : int, optional
+     Pooling size for the average pooling layers. Default is 8.
+ inception_windows : list[float], optional
+     List of window sizes (in seconds) for the inception modules.
+     Default is [0.5, 0.25, 0.125].
+
+ Notes
+ -----
+ This implementation is not guaranteed to be correct, has not been checked
+ by original authors. The modifications are minimal and the model is expected
+ to work as intended. the original code from [code2020]_.
+
+ References
+ ----------
+ .. [ding2020] Ding, Y., Robinson, N., Zeng, Q., Chen, D., Wai, A. A. P.,
+     Lee, T. S., & Guan, C. (2020, July). Tsception: a deep learning framework
+     for emotion detection using EEG. In 2020 international joint conference
+     on neural networks (IJCNN) (pp. 1-7). IEEE.
+ .. [code2020] Ding, Y., Robinson, N., Zeng, Q., Chen, D., Wai, A. A. P.,
+     Lee, T. S., & Guan, C. (2020, July). Tsception: a deep learning framework
+     for emotion detection using EEG.
+     https://github.com/deepBrains/TSception/blob/master/Models.py
+
+ .. 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 TSception
+
+     # Train your model
+     model = TSception(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-tsception-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import TSception
+
+     # Load pretrained model
+     model = TSception.from_pretrained("username/my-tsception-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = TSception.from_pretrained("username/my-tsception-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 = TSception.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.