- 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)¶
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.
Number of EEG channels.
EEG sampling frequency.
Number of convolutional channels. Set to 20 in [Blanco2020].
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.
Size of the input, in seconds.
Number of classes.
Dropout rate before the output dense layer.
If True, apply batch normalization after both temporal convolutional layers.
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). https://doi.org/10.1007/s00500-019-04174-1
Forward pass. Parameters ———- x: torch.Tensor
Batch of EEG windows of shape (batch_size, n_channels, n_times).