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Architecture-only repository. Documents thebraindecode.models.SleepStagerChambon2018class. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import SleepStagerChambon2018
2
3model = SleepStagerChambon2018(
4 n_chans=2,
5 sfreq=100,
6 input_window_seconds=30.0,
7 n_outputs=5,
8)
| Parameter | Type | Description |
|---|---|---|
n_conv_chs | int | Number of convolutional channels. Set to 8 in [Chambon2018]. |
time_conv_size_s | float | Size of filters in temporal convolution layers, in seconds. Set to 0.5 in [Chambon2018] (64 samples at sfreq=128). |
max_pool_size_s | float | Max pooling size, in seconds. Set to 0.125 in [Chambon2018] (16 samples at sfreq=128). |
pad_size_s | float | Padding size, in seconds. Set to 0.25 in [Chambon2018] (half the temporal convolution kernel size). |
drop_prob | float | Dropout rate before the output dense layer. |
apply_batch_norm | bool | If True, apply batch normalization after both temporal convolutional layers. |
return_feats | bool | If True, return the features, i.e. the output of the feature extractor (before the final linear layer). If False, pass the features through the final linear layer. |
n_channels | int | Alias for n_chans. |
input_size_s: | — | Alias for input_window_seconds. |
n_classes: | — | Alias for n_outputs. |
activation: nn.Module, default=nn.ReLU | — | Activation function class to apply. Should be a PyTorch activation module class like nn.ReLU or nn.ELU. Default is nn.ReLU. |
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}