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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) [ingolfsson2020].
> **Architecture-only repository.** Documents the
> `braindecode.models.EEGTCNet` 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 EEGTCNet
model = EEGTCNet(
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.EEGTCNet.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/eegtcnet.py#L15>
## Architecture
## Parameters
| Parameter | Type | Description |
|---|---|---|
| `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
1. 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
## 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.
|