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

# EEGTCNet

EEGTCNet model from Ingolfsson et al (2020) .

> **Architecture-only repository.** This repo documents the
> `braindecode.models.EEGTCNet` 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.

## Quick start

```bash
pip install braindecode
```

```python
from braindecode.models import EEGTCNet

model = EEGTCNet(
    n_chans=22,
    sfreq=250,
    input_window_seconds=4.0,
    n_outputs=4,
)
```

The signal-shape arguments above are example defaults — adjust them
to match your recording.

## Documentation

- Full API reference (parameters, references, architecture figure):
  <https://braindecode.org/stable/generated/braindecode.models.EEGTCNet.html>
- Interactive browser with live instantiation:
  <https://huggingface.co/spaces/braindecode/model-explorer>
- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/eegtcnet.py#L15>

## Architecture description

The block below is the rendered class docstring (parameters,
references, architecture figure where available).

<div class='bd-doc'><main>
<p>EEGTCNet model from Ingolfsson et al (2020) [ingolfsson2020]_.</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><span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#6c757d;color:white;font-size:11px;font-weight:600;margin-right:4px;">Recurrent</span>



 .. figure:: https://braindecode.org/dev/_static/model/eegtcnet.jpg
     :align: center
     :alt: EEGTCNet Architecture

 Combining EEGNet and TCN blocks.

 Parameters
 ----------
 activation : nn.Module, optional
     Activation function to use. Default is `nn.ELU()`.
 depth_multiplier : int, optional
     Depth multiplier for the depthwise convolution. Default is 2.
 filter_1 : int, optional
     Number of temporal filters in the first convolutional layer. Default is 8.
 kern_length : int, optional
     Length of the temporal kernel in the first convolutional layer. Default is 64.
 dropout : float, optional
     Dropout rate. Default is 0.5.
 depth : int, optional
     Number of residual blocks in the TCN. Default is 2.
 kernel_size : int, optional
     Size of the temporal convolutional kernel in the TCN. Default is 4.
 filters : int, optional
     Number of filters in the TCN convolutional layers. Default is 12.
 max_norm_const : float
     Maximum L2-norm constraint imposed on weights of the last
     fully-connected layer. Defaults to 0.25.

 References
 ----------
 .. [ingolfsson2020] Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N.,
     Cavigelli, L., & Benini, L. (2020). EEG-TCNet: An accurate temporal
     convolutional network for embedded motor-imagery brain–machine interfaces.
     https://doi.org/10.48550/arXiv.2006.00622

 .. 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 EEGTCNet

     # Train your model
     model = EEGTCNet(n_chans=22, n_outputs=4, n_times=1000)
     # ... training code ...

     # Push to the Hub
     model.push_to_hub(
         repo_id="username/my-eegtcnet-model",
         commit_message="Initial model upload",
     )

 **Loading a model from the Hub:**

 .. code::
     from braindecode.models import EEGTCNet

     # Load pretrained model
     model = EEGTCNet.from_pretrained("username/my-eegtcnet-model")

     # Load with a different number of outputs (head is rebuilt automatically)
     model = EEGTCNet.from_pretrained("username/my-eegtcnet-model", n_outputs=4)

 **Extracting features and replacing the head:**

 .. 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"]

     # Replace the classification head
     model.reset_head(n_outputs=10)

 **Saving and restoring full configuration:**

 .. code::
     import json

     config = model.get_config()            # all __init__ params
     with open("config.json", "w") as f:
         json.dump(config, f)

     model2 = EEGTCNet.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:

```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.