BDiscriminator

class torcheeg.models.BDiscriminator(in_channels: int = 4, grid_size: Tuple[int, int] = (9, 9), hid_channels: int = 64)

TorchEEG provides an EEG feature generator based on CNN architecture and GAN for generating EEG grid representations of different frequency bands based on a given class label.

g_model = BGenerator(in_channels=128)
d_model = BDiscriminator(in_channels=4)
z = torch.normal(mean=0, std=1, size=(1, 128))
fake_X = g_model(z)
disc_X = d_model(fake_X)

Parameters:

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

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

• hid_channels (int) – The number of hidden nodes in the first fully connected layer. (default: 32)

forward(x: Tensor)

Parameters:

x (torch.Tensor) – EEG signal representation, the ideal input shape is [n, 4, 9, 9]. Here, n corresponds to the batch size, 4 corresponds to the in_channels, and (9, 9) corresponds to the grid_size.

Returns:

Predicts the result of whether a given sample is a fake sample or not. Here, n corresponds to the batch size.

Return type:

torch.Tensor[n, 1]