README.md

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

# Deep4Net

Deep ConvNet model from Schirrmeister et al (2017) [Schirrmeister2017].

> **Architecture-only repository.** Documents the
> `braindecode.models.Deep4Net` 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 Deep4Net

model = Deep4Net(
    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.Deep4Net.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/deep4.py#L19>


## Architecture

![Deep4Net architecture](https://onlinelibrary.wiley.com/cms/asset/fc200ccc-d8c4-45b4-8577-56ce4d15999a/hbm23730-fig-0001-m.jpg)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `final_conv_length: int | str` | — | Length of the final convolution layer. If set to "auto", n_times must not be None. Default: "auto". |
| `n_filters_time: int` | — | Number of temporal filters. |
| `n_filters_spat: int` | — | Number of spatial filters. |
| `filter_time_length: int` | — | Length of the temporal filter in layer 1. |
| `pool_time_length: int` | — | Length of temporal pooling filter. |
| `pool_time_stride: int` | — | Length of stride between temporal pooling filters. |
| `n_filters_2: int` | — | Number of temporal filters in layer 2. |
| `filter_length_2: int` | — | Length of the temporal filter in layer 2. |
| `n_filters_3: int` | — | Number of temporal filters in layer 3. |
| `filter_length_3: int` | — | Length of the temporal filter in layer 3. |
| `n_filters_4: int` | — | Number of temporal filters in layer 4. |
| `filter_length_4: int` | — | Length of the temporal filter in layer 4. |
| `activation_first_conv_nonlin: nn.Module, default is nn.ELU` | — | Non-linear activation function to be used after convolution in layer 1. |
| `first_pool_mode: str` | — | Pooling mode in layer 1. "max" or "mean". |
| `first_pool_nonlin: callable` | — | Non-linear activation function to be used after pooling in layer 1. |
| `activation_later_conv_nonlin: nn.Module, default is nn.ELU` | — | Non-linear activation function to be used after convolution in later layers. |
| `later_pool_mode: str` | — | Pooling mode in later layers. "max" or "mean". |
| `later_pool_nonlin: callable` | — | Non-linear activation function to be used after pooling in later layers. |
| `drop_prob: float` | — | Dropout probability. |
| `split_first_layer: bool` | — | Split first layer into temporal and spatial layers (True) or just use temporal (False). There would be no non-linearity between the split layers. |
| `batch_norm: bool` | — | Whether to use batch normalisation. |
| `batch_norm_alpha: float` | — | Momentum for BatchNorm2d. |
| `stride_before_pool: bool` | — | Stride before pooling. |


## References

1. Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730


## 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.