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Architecture-only repository. Documents thebraindecode.models.FBLightConvNetclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import FBLightConvNet
2
3model = FBLightConvNet(
4 n_chans=22,
5 sfreq=250,
6 input_window_seconds=4.0,
7 n_outputs=4,
8)
| Parameter | Type | Description |
|---|---|---|
n_bands | int or None or list of tuple of int, default=8 | Number of frequency bands or a list of frequency band tuples. If a list of tuples is provided, each tuple defines the lower and upper bounds of a frequency band. |
n_filters_spat | int, default=32 | Number of spatial filters in the depthwise convolutional layer. |
n_dim | int, default=3 | Number of dimensions for the temporal reduction layer. |
stride_factor | int, default=4 | Stride factor used for reshaping the temporal dimension. |
activation | nn.Module, default=nn.ELU | Activation function class to apply after convolutional layers. |
verbose | bool, default=False | If True, enables verbose output during filter creation using mne. |
filter_parameters | dict, default={} | Additional parameters for the FilterBankLayer. |
heads | int, default=8 | Number of attention heads in the multi-head attention mechanism. |
weight_softmax | bool, default=True | If True, applies softmax to the attention weights. |
bias | bool, default=False | If True, includes a bias term in the convolutional layers. |
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}