---
license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
  - eeg
  - biosignal
  - pytorch
  - neuroscience
  - braindecode
  - convolutional
---

# EEGITNet

EEG-ITNet from Salami, et al (2022) [Salami2022]

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

model = EEGITNet(
    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.EEGITNet.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/eegitnet.py#L12>


## Architecture

![EEGITNet architecture](https://braindecode.org/dev/_static/model/eegitnet.jpg)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `drop_prob: float` | — | Dropout probability. |
| `activation: nn.Module, default=nn.ELU` | — | Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.ELU`. |
| `kernel_length` | int, optional | Kernel length for inception branches. Determines the temporal receptive field. Default is 16. |
| `pool_kernel` | int, optional | Pooling kernel size for the average pooling layer. Default is 4. |
| `tcn_in_channel` | int, optional | Number of input channels for Temporal Convolutional (TC) blocks. Default is 14. |
| `tcn_kernel_size` | int, optional | Kernel size for the TC blocks. Determines the temporal receptive field. Default is 4. |
| `tcn_padding` | int, optional | Padding size for the TC blocks to maintain the input dimensions. Default is 3. |
| `drop_prob` | float, optional | Dropout probability applied after certain layers to prevent overfitting. Default is 0.4. |
| `tcn_dilatation` | int, optional | Dilation rate for the first TC block. Subsequent blocks will have dilation rates multiplied by powers of 2. Default is 1. |


## References

1. A. Salami, J. Andreu-Perez and H. Gillmeister, "EEG-ITNet: An Explainable Inception Temporal Convolutional Network for motor imagery classification," in IEEE Access, doi: 10.1109/ACCESS.2022.3161489.


## 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.