FBCNet

FBCNet from Mane, R et al (2021) [fbcnet2021].

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

model = FBCNet(
    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.FBCNet.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/fbcnet.py#L31

Architecture

Parameters

Parameter	Type	Description
n_bands	int or None or list[tuple[int, int]]], default=9	Number of frequency bands. Could
n_filters_spat	int, default=32	Number of spatial filters for the first convolution.
n_dim: int, default=3	—	Number of dimensions for the temporal reductor
temporal_layer	str, default='LogVarLayer'	Type of temporal aggregator layer. Options: 'VarLayer', 'StdLayer', 'LogVarLayer', 'MeanLayer', 'MaxLayer'.
stride_factor	int, default=4	Stride factor for reshaping.
activation	nn.Module, default=nn.SiLU	Activation function class to apply in Spatial Convolution Block.
cnn_max_norm	float, default=2.0	Maximum norm for the spatial convolution layer.
linear_max_norm	float, default=0.5	Maximum norm for the final linear layer.
filter_parameters: dict, default None	—	Dictionary of parameters to use for the FilterBankLayer. If None, a default Chebyshev Type II filter with transition bandwidth of 2 Hz and stop-band ripple of 30 dB will be used.

References

1. Mane, R., Chew, E., Chua, K., Ang, K. K., Robinson, N., Vinod, A. P., ... & Guan, C. (2021). FBCNet: A multi-view convolutional neural network for brain-computer interface. preprint arXiv:2104.01233.
2. Link to source-code: https://github.com/ravikiran-mane/FBCNet

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.