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Architecture-only repository. Documents thebraindecode.models.EEGITNetclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import EEGITNet
2
3model = EEGITNet(
4 n_chans=22,
5 sfreq=250,
6 input_window_seconds=4.0,
7 n_outputs=4,
8)
| 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. |
1@article{aristimunha2025braindecode,
2 title = {Braindecode: a deep learning library for raw electrophysiological data},
3 author = {Aristimunha, Bruno and others},
4 journal = {Zenodo},
5 year = {2025},
6 doi = {10.5281/zenodo.17699192},
7}