braindecode.models.SleepStagerChambon2018#

class braindecode.models.SleepStagerChambon2018(n_chans=None, sfreq=None, n_conv_chs=8, time_conv_size_s=0.5, max_pool_size_s=0.125, pad_size_s=0.25, activation: ~torch.nn.modules.module.Module = <class 'torch.nn.modules.activation.ReLU'>, input_window_seconds=None, n_outputs=5, drop_prob=0.25, apply_batch_norm=False, return_feats=False, chs_info=None, n_times=None)[source]#

Sleep staging architecture from Chambon et al. (2018) [Chambon2018].

SleepStagerChambon2018 Architecture

Convolutional neural network for sleep staging described in [Chambon2018].

Parameters:

  • n_chans (int) – Number of EEG channels.

  • sfreq (float) – Sampling frequency of the EEG recordings.

  • 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).

  • 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.

  • input_window_seconds (float) – Length of the input window in seconds.

  • n_outputs (int) – Number of outputs of the model. This is the number of classes in the case of classification.

  • 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.

  • chs_info (list of dict) – Information about each individual EEG channel. This should be filled with info["chs"]. Refer to mne.Info for more details.

  • n_times (int) – Number of time samples of the input window.

Raises:

  • ValueError – If some input signal-related parameters are not specified: and can not be inferred.

  • FutureWarning – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.

Notes

If some input signal-related parameters are not specified, there will be an attempt to infer them from the other parameters.

References

[Chambon2018] (1,2,3,4,5,6)

Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., & Gramfort, A. (2018). A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(4), 758-769.

Methods

forward(x)[source]#

Forward pass.

Parameters:

x (torch.Tensor) – Batch of EEG windows of shape (batch_size, n_channels, n_times).

Examples using braindecode.models.SleepStagerChambon2018#

Self-supervised learning on EEG with relative positioning

Self-supervised learning on EEG with relative positioning

Sleep staging on the Sleep Physionet dataset using Chambon2018 network

Sleep staging on the Sleep Physionet dataset using Chambon2018 network