LGGNet

class torcheeg.models.LGGNet(region_list, in_channels: int = 1, num_electrodes: int = 32, chunk_size: int = 128, sampling_rate: int = 128, num_T: int = 64, hid_channels: int = 32, dropout: float = 0.5, pool_kernel_size: int = 16, pool_stride: int = 4, num_classes: int = 2)

DLocal-Global-Graph Networks (LGGNet). For more details, please refer to the following information.

• Paper: Ding Y, Robinson N, Zeng Q, et al. LGGNet: learning from Local-global-graph representations for brain-computer interface[J]. arXiv preprint arXiv:2105.02786, 2021.

• URL: https://arxiv.org/abs/2105.02786

• Related Project: https://github.com/yi-ding-cs/LGG

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

from torcheeg.datasets import SEEDDataset
from torcheeg.models import LGGNet
from torcheeg import transforms
from torcheeg.datasets.constants import SEED_GENERAL_REGION_LIST

dataset = SEEDDataset(root_path='./Preprocessed_EEG',
                      offline_transform=transforms.Compose([
                          transforms.MeanStdNormalize(),
                          transforms.To2d()
                      ]),
                      online_transform=transforms.Compose([
                          transforms.ToTensor()
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select('emotion'),
                          transforms.Lambda(lambda x: x + 1)
                      ]))
model = LGGNet(region_list=SEED_GENERAL_REGION_LIST, chunk_size=128, num_electrodes=32, hid_channels=32, num_classes=2)

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

The current built-in region_list includs:

• torcheeg.datasets.constants.emotion_recognition.amigos.AMIGOS_GENERAL_REGION_LIST

• torcheeg.datasets.constants.emotion_recognition.amigos.AMIGOS_FRONTAL_REGION_LIST

• torcheeg.datasets.constants.emotion_recognition.amigos.AMIGOS_HEMISPHERE_REGION_LIST

• torcheeg.datasets.constants.emotion_recognition.deap.DEAP_GENERAL_REGION_LIST

• …

• torcheeg.datasets.constants.emotion_recognition.dreamer.DREAMER_GENERAL_REGION_LIST

• …

• torcheeg.datasets.constants.emotion_recognition.mahnob.MAHNOB_GENERAL_REGION_LIST

• …

• torcheeg.datasets.constants.emotion_recognition.seed.SEED_GENERAL_REGION_LIST

• …

Parameters:

• region_list (list) – The local graph structure defined according to the 10-20 system, where the electrodes are divided into different brain regions.

• in_channels (int) – The feature dimension of each electrode. (default: 1)

• num_electrodes (int) – The number of electrodes. (default: 32)

• chunk_size (int) – Number of data points included in each EEG chunk. (default: 128)

• sampling_rate (int) – The sampling rate of the EEG signals, i.e., \(f_s\) in the paper. (default: 128)

• num_T (int) – The number of multi-scale 1D temporal kernels in the dynamic temporal layer, i.e., \(T\) kernels in the paper. (default: 64)

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

• dropout (float) – Probability of an element to be zeroed in the dropout layers. (default: 0.5)

• pool_kernel_size (int) – The kernel size of pooling layers in the temporal blocks (default: 16)

• pool_stride (int) – The stride of pooling layers in the temporal blocks (default: 4)

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

forward(x)

Parameters:

x (torch.Tensor) – EEG signal representation, the ideal input shape is [n, 1, 32, 128]. Here, n corresponds to the batch size, 32 corresponds to num_electrodes, and chunk_size corresponds to chunk_size.

Returns:

the predicted probability that the samples belong to the classes.

Return type:

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