IFNet

IFNetV2 from Wang J et al (2023) [ifnet].

Architecture-only repository. Documents the braindecode.models.IFNet 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 IFNet

model = IFNet(
    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.IFNet.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/ifnet.py#L31

Architecture

Parameters

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.

References

1. Wang, J., Yao, L., & Wang, Y. (2023). IFNet: An interactive frequency convolutional neural network for enhancing motor imagery decoding from EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 1900-1911.
2. Wang, J., Yao, L., & Wang, Y. (2023). IFNet: An interactive frequency convolutional neural network for enhancing motor imagery decoding from EEG. https://github.com/Jiaheng-Wang/IFNet

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.