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Architecture-only repository. Documents thebraindecode.models.TIDNetclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import TIDNet
2
3model = TIDNet(
4 n_chans=22,
5 sfreq=250,
6 input_window_seconds=4.0,
7 n_outputs=4,
8)
| Parameter | Type | Description |
|---|---|---|
s_growth | int | DenseNet-style growth factor (added filters per DenseFilter) |
t_filters | int | Number of temporal filters. |
drop_prob | float | Dropout probability |
pooling | int | Max temporal pooling (width and stride) |
temp_layers | int | Number of temporal layers |
spat_layers | int | Number of DenseFilters |
temp_span | float | Percentage of n_times that defines the temporal filter length: temp_len = ceil(temp_span * n_times) e.g A value of 0.05 for temp_span with 1500 n_times will yield a temporal filter of length 75. |
bottleneck | int | Bottleneck factor within Densefilter |
summary | int | Output size of AdaptiveAvgPool1D layer. If set to -1, value will be calculated automatically (n_times // pooling). |
in_chans | — | Alias for n_chans. |
n_classes: | — | Alias for n_outputs. |
input_window_samples | — | Alias for n_times. |
activation: nn.Module, default=nn.LeakyReLU | — | Activation function class to apply. Should be a PyTorch activation module class like nn.ReLU or nn.ELU. Default is nn.LeakyReLU. |
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}