README.md

---
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 [ding2020].

> **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.

## 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 illustrative defaults — adjust to
match your recording.

## Documentation
- 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

![TSception architecture](https://user-images.githubusercontent.com/58539144/74716976-80415e00-526a-11ea-9433-02ab2b753f6b.PNG)


## Parameters

| 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]. |


## References

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


## Citation

Cite the original architecture paper (see *References* 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.