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

pip install braindecode

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

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

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:

@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.

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