CCNN

class torcheeg.models.CCNN(in_channels: int = 4, grid_size: Tuple[int, int] = (9, 9), num_classes: int = 2, dropout: float = 0.5)

Continuous Convolutional Neural Network (CCNN). For more details, please refer to the following information.

• Paper: Yang Y, Wu Q, Fu Y, et al. Continuous convolutional neural network with 3D input for EEG-based emotion recognition[C]//International Conference on Neural Information Processing. Springer, Cham, 2018: 433-443.

• URL: https://link.springer.com/chapter/10.1007/978-3-030-04239-4_39

• Related Project: https://github.com/ynulonger/DE_CNN

Below is a recommended suite for use in emotion recognition tasks:

dataset = DEAPDataset(io_path=f'./deap',
            root_path='./data_preprocessed_python',
            offline_transform=transforms.Compose([
                transforms.BandDifferentialEntropy(),
                transforms.ToGrid(DEAP_CHANNEL_LOCATION_DICT)
            ]),
            online_transform=transforms.ToTensor(),
            label_transform=transforms.Compose([
                transforms.Select('valence'),
                transforms.Binary(5.0),
            ]))
model = CCNN(num_classes=2, in_channels=4, grid_size=(9, 9))

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))

• num_classes (int) – The number of classes to predict. (default: 2)

• dropout (float) – Probability of an element to be zeroed in the dropout layers. (default: 0.25)

forward(x: Tensor) → 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 in_channels, and (9, 9) corresponds to grid_size.

Returns:

the predicted probability that the samples belong to the classes.

Return type:

torch.Tensor[number of sample, number of classes]