README.md

---
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) [ctnet].

> **Architecture-only repository.** Documents the
> `braindecode.models.CTNet` 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 CTNet

model = CTNet(
    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.CTNet.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/ctnet.py#L27>


## Architecture

![CTNet architecture](https://raw.githubusercontent.com/snailpt/CTNet/main/architecture.png)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `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. |


## References

1. 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.
2. Zhao, W., Jiang, X., Zhang, B., Xiao, S., & Weng, S. (2024). CTNet source code: https://github.com/snailpt/CTNet


## 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.