RGNN

class torcheeg.models.pyg.RGNN(adj: Tensor | list, num_electrodes: int = 62, in_channels: int = 5, num_layers: int = 2, hid_channels: int = 32, num_classes: int = 3, dropout: float = 0.7, learn_edge_weights: bool = True)

Regularized Graph Neural Networks (RGNN). For more details, please refer to the following information.

• Paper: Zhong P, Wang D, Miao C. EEG-based emotion recognition using regularized graph neural networks[J]. IEEE Transactions on Affective Computing, 2020.

• URL: https://ieeexplore.ieee.org/abstract/document/9091308

• Related Project: https://github.com/zhongpeixiang/RGNN

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

from torcheeg.datasets import SEEDDataset
from torcheeg.models import RGNN
from torcheeg.transforms.pyg import ToG
from torcheeg.datasets.constants import SEED_STANDARD_ADJACENCY_MATRIX
from torch_geometric.data import DataLoader


dataset = SEEDDataset(root_path='./Preprocessed_EEG',
                      offline_transform=transforms.BandDifferentialEntropy(),
                      online_transform=ToG(SEED_STANDARD_ADJACENCY_MATRIX),
                      label_transform=transforms.Compose([
                          transforms.Select('emotion'),
                          transforms.Lambda(lambda x: int(x) + 1),
                      ]),
                      num_worker=8)

model = RGNN(adj=torch.Tensor(SEED_STANDARD_ADJACENCY_MATRIX),
             in_channels=5,
             num_electrodes=62,
             hid_channels=32,
             num_layers=2,
             num_classes=3,
             dropout=0.7,
             learn_edge_weights=True)

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

Parameters:

• adj (torch.Tensor) – The adjacency matrix corresponding to the EEG representation, where 1.0 means the node is adjacent and 0.0 means the node is not adjacent. The matrix shape should be [num_electrodes, num_electrodes].

• num_electrodes (int) – The number of electrodes. (default: 62)

• in_channels (int) – The feature dimension of each electrode. (default: 5)

• num_layers (int) – The number of graph convolutional layers. (default: 2)

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

• num_classes (int) – The number of classes to predict. (default: 3)

• dropout (float) – Probability of an element to be zeroed in the dropout layers at the output fully-connected layer. (default: 0.7)

• learn_edge_weights (bool) – Whether to learn a set of parameters to adjust the adjacency matrix. (default: True)

forward(data: Batch) → Tensor

Parameters:

data (torch_geometric.data.Batch) – EEG signal representation, the ideal input shape of data.x is [n, 62, 4]. Here, n corresponds to the batch size, 62 corresponds to the number of electrodes, and 4 corresponds to in_channels.

Returns:

the predicted probability that the samples belong to the classes.

Return type:

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