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Architecture-only repository. Documents thebraindecode.models.IFNetclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import IFNet
2
3model = IFNet(
4 n_chans=22,
5 sfreq=250,
6 input_window_seconds=4.0,
7 n_outputs=4,
8)
| Parameter | Type | Description |
|---|---|---|
bands | list[tuple[int, int]] or int or None, default=[[4, 16], (16, 40)] | Frequency bands for filtering. |
out_planes | int, default=64 | Number of output feature dimensions. |
kernel_sizes | tuple of int, default=(63, 31) | List of kernel sizes for temporal convolutions. |
patch_size | int, default=125 | Size of the patches for temporal segmentation. |
drop_prob | float, default=0.5 | Dropout probability. |
activation | nn.Module, default=nn.GELU | Activation function after the InterFrequency Layer. |
verbose | bool, default=False | Verbose to control the filtering layer |
filter_parameters | dict, default={} | Additional parameters for the filter bank layer. |
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}