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README.md

@@ -13,13 +13,12 @@ tags:
 
 # TSception
 
-TSception model from Ding et al. (2020) from .
+TSception model from Ding et al. (2020) from [ding2020].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** 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.
+> distributed here.** Instantiate the model and train it on your own
+> data.
 
 ## Quick start
 
@@ -38,143 +37,43 @@ model = TSception(
 )
 ```
 
-The signal-shape arguments above are example defaults — adjust them
-to match your recording.
+The signal-shape arguments above are illustrative defaults — adjust 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:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.TSception.html>
+- Interactive browser (live instantiation, parameter counts):
   <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)
+## Architecture
 
- **Extracting features and replacing the head:**
+![TSception architecture](https://user-images.githubusercontent.com/58539144/74716976-80415e00-526a-11ea-9433-02ab2b753f6b.PNG)
 
- .. 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"]
+## Parameters
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
+| Parameter | Type | Description |
+|---|---|---|
+| `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]. |
 
- **Saving and restoring full configuration:**
 
- .. code::
-     import json
+## References
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
+1. 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.
+2. 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
 
-     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:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,