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

pip install braindecode

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:

@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.