TSCeption

class torcheeg.models.TSCeption(num_electrodes: int = 28, num_T: int = 15, num_S: int = 15, in_channels: int = 1, hid_channels: int = 32, num_classes: int = 2, sampling_rate: int = 128, dropout: float = 0.5)

TSCeption. For more details, please refer to the following information.

• Paper: Ding Y, Robinson N, Zhang S, et al. Tsception: Capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition[J]. arXiv preprint arXiv:2104.02935, 2021.

• URL: https://arxiv.org/abs/2104.02935

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

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_LIST
from torcheeg.models import TSCeption
from torch.utils.data import DataLoader

dataset = DEAPDataset(root_path='./data_preprocessed_python',
                      chunk_size=512,
                      num_baseline=1,
                      baseline_chunk_size=512,
                      offline_transform=transforms.Compose([
                          transforms.PickElectrode(PickElectrode.to_index_list(
                          ['FP1', 'AF3', 'F3', 'F7',
                          'FC5', 'FC1', 'C3', 'T7',
                          'CP5', 'CP1', 'P3', 'P7',
                          'PO3','O1', 'FP2', 'AF4',
                          'F4', 'F8', 'FC6', 'FC2',
                          'C4', 'T8', 'CP6', 'CP2',
                          'P4', 'P8', 'PO4', 'O2'], DEAP_CHANNEL_LIST)),
                          transforms.To2d()
                      ]),
                      online_transform=transforms.ToTensor(),
                      label_transform=transforms.Compose([
                          transforms.Select('valence'),
                          transforms.Binary(5.0),
                      ]))

model = TSCeption(num_classes=2,
                  num_electrodes=28,
                  sampling_rate=128,
                  num_T=15,
                  num_S=15,
                  hid_channels=32,
                  dropout=0.5)

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

Parameters:

• num_electrodes (int) – The number of electrodes. (default: 28)

• num_T (int) – The number of multi-scale 1D temporal kernels in the dynamic temporal layer, i.e., \(T\) kernels in the paper. (default: 15)

• num_S (int) – The number of multi-scale 1D spatial kernels in the asymmetric spatial layer. (default: 15)

• in_channels (int) – The number of channels of the signal corresponding to each electrode. If the original signal is used as input, in_channels is set to 1; if the original signal is split into multiple sub-bands, in_channels is set to the number of bands. (default: 1)

• 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: 2)

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

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

forward(x: Tensor) → Tensor

Parameters:

x (torch.Tensor) – EEG signal representation, the ideal input shape is [n, 1, 28, 512]. Here, n corresponds to the batch size, 1 corresponds to number of channels for convolution, 28 corresponds to num_electrodes, and 512 corresponds to the input dimension for each electrode.

Returns:

the predicted probability that the samples belong to the classes.

Return type:

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