FBCCNN

class torcheeg.models.FBCCNN(in_channels: int = 4, grid_size: Tuple[int, int] = (9, 9), num_classes: int = 2)

Frequency Band Correlation Convolutional Neural Network (FBCCNN). For more details, please refer to the following information.

• Paper: Pan B, Zheng W. Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network[J]. Computational and Mathematical Methods in Medicine, 2021.

• URL: https://www.hindawi.com/journals/cmmm/2021/2520394/

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

from torcheeg.datasets import DEAPDataset
from torcheeg import transforms
from torcheeg.datasets.constants import DEAP_CHANNEL_LOCATION_DICT
from torcheeg.models import FBCCNN
from torch.utils.data import DataLoader

dataset = DEAPDataset(root_path='./data_preprocessed_python',
                      online_transform=transforms.Compose([
                          transforms.BandPowerSpectralDensity(),
                          transforms.ToGrid(DEAP_CHANNEL_LOCATION_DICT)
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select('valence'),
                          transforms.Binary(5.0),
                      ]))

model = FBCCNN(num_classes=2, in_channels=4, grid_size=(9, 9))

x, y = next(iter(DataLoader(dataset, batch_size=64)))
model(x)

Parameters:

• in_channels (int) – The feature dimension of each electrode, i.e., \(N\) in the paper. (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)

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]