class braindecode.models.SleepStagerBlanco2020(n_channels, sfreq, n_conv_chans=20, input_size_s=30, n_classes=5, n_groups=2, max_pool_size=2, dropout=0.5, apply_batch_norm=False, return_feats=False)[source]#

Sleep staging architecture from Blanco et al 2020.

Convolutional neural network for sleep staging described in [Blanco2020]. A series of seven convolutional layers with kernel sizes running down from 7 to 3, in an attempt to extract more general features at the beginning, while more specific and complex features were extracted in the final stages.

  • n_channels (int) – Number of EEG channels.

  • sfreq (float) – EEG sampling frequency.

  • n_conv_chans (int) – Number of convolutional channels. Set to 20 in [Blanco2020].

  • n_groups (int) – Number of groups for the convolution. Set to 2 in [Blanco2020] for 2 Channel EEG. controls the connections between inputs and outputs. n_channels and n_conv_chans must be divisible by n_groups.

  • input_size_s (float) – Size of the input, in seconds.

  • n_classes (int) – Number of classes.

  • dropout (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.



Fernandez-Blanco, E., Rivero, D. & Pazos, A. Convolutional neural networks for sleep stage scoring on a two-channel EEG signal. Soft Comput 24, 4067–4079 (2020).



Forward pass. :param x: Batch of EEG windows of shape (batch_size, n_channels, n_times). :type x: torch.Tensor