BCDecoder

class torcheeg.models.BCDecoder(in_channels: int = 64, out_channels: int = 4, grid_size: Tuple[int, int] = (9, 9), num_classes: int = 3)

TorchEEG provides an EEG feature decoder based on CNN architecture and CVAE for generating EEG grid representations of different frequency bands based on a given class label. In particular, the expected labels are additionally provided to guide the decoder to reconstruct samples of the specified class.

• Related Project: https://github.com/timbmg/VAE-CVAE-MNIST/blob/master/models.py

encoder = BCEncoder(in_channels=4, num_classes=3)
decoder = BCDecoder(in_channels=64, out_channels=4, num_classes=3)
y = torch.randint(low=0, high=3, size=(1,))
mock_eeg = torch.randn(1, 4, 9, 9)
mu, logvar = encoder(mock_eeg, y)
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
z = eps * std + mu
fake_X = decoder(z, y)

Parameters:

• in_channels (int) – The input feature dimension (of noise vectors). (default: 64)

• out_channels (int) – The generated feature dimension of each electrode. (default: 4)

• grid_size (tuple) – Spatial dimensions of grid-like EEG representation. (default: (9, 9))

forward(x: Tensor, y: Tensor)

Parameters:

• x (torch.Tensor) – Given the mean and standard deviation vectors, the feature vector z obtained using the reparameterization technique. The shapes of the feature vector should be [n, 64]. Here, n corresponds to the batch size, and 64 corresponds to in_channels.

• y (torch.Tensor) – Category labels (int) for a batch of samples The shape should be [n,]. Here, n corresponds to the batch size.

Returns:

the decoded results, which should have the same shape as the input noise, i.e., [n, 4, 9, 9]. Here, n corresponds to the batch size, 4 corresponds to in_channels, and (9, 9) corresponds to grid_size.

Return type:

torch.Tensor[n, 4, 9, 9]