Recurrent Neural Networks

torcheeg.models.GRU

class torcheeg.models.GRU(num_electrodes: int = 32, hid_channels: int = 64, num_classes: int = 2)

Bases: Module

A simple but effective gate recurrent unit (GRU) network structure from the book of Zhang et al. For more details, please refer to the following information.

• Book: Zhang X, Yao L. Deep Learning for EEG-Based Brain-Computer Interfaces: Representations, Algorithms and Applications[M]. 2021.

• URL: https://www.worldscientific.com/worldscibooks/10.1142/q0282#t=aboutBook

• Related Project: https://github.com/xiangzhang1015/Deep-Learning-for-BCI/blob/master/pythonscripts/4-1-2_GRU.py

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

dataset = DEAPDataset(io_path=f'./deap',
            root_path='./data_preprocessed_python',
            online_transform=transforms.ToTensor(),
            label_transform=transforms.Compose([
                transforms.Select('valence'),
                transforms.Binary(5.0),
            ]))
model = GRU(num_electrodes=32, hid_channels=64, num_classes=2)

Parameters

• num_electrodes (int) – The number of electrodes, i.e., \(C\) in the paper. (defualt: 32)

• hid_channels (int) – The number of hidden nodes in the GRU layers and the fully connected layer. (defualt: 64)

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

forward(x: Tensor) → Tensor

Parameters

x (torch.Tensor) – EEG signal representation, the ideal input shape is [n, 32, 128]. Here, n corresponds to the batch size, 32 corresponds to num_electrodes, and 128 corresponds to the number of data points included in the input EEG chunk.

Returns

the predicted probability that the samples belong to the classes.

Return type

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

training: bool

torcheeg.models.LSTM

class torcheeg.models.LSTM(num_electrodes: int = 32, hid_channels: int = 64, num_classes: int = 2)

Bases: Module

A simple but effective long-short term memory (LSTM) network structure from the book of Zhang et al. For more details, please refer to the following information.

• Book: Zhang X, Yao L. Deep Learning for EEG-Based Brain-Computer Interfaces: Representations, Algorithms and Applications[M]. 2021.

• URL: https://www.worldscientific.com/worldscibooks/10.1142/q0282#t=aboutBook

• Related Project: https://github.com/xiangzhang1015/Deep-Learning-for-BCI/blob/master/pythonscripts/4-1-1_LSTM.py

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

dataset = DEAPDataset(io_path=f'./deap',
            root_path='./data_preprocessed_python',
            online_transform=transforms.ToTensor(),
            label_transform=transforms.Compose([
                transforms.Select('valence'),
                transforms.Binary(5.0),
            ]))
model = GRU(num_electrodes=32, hid_channels=64, num_classes=2)

Parameters

• num_electrodes (int) – The number of electrodes, i.e., \(C\) in the paper. (defualt: 32)

• hid_channels (int) – The number of hidden nodes in the GRU layers and the fully connected layer. (defualt: 64)

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

forward(x: Tensor) → Tensor

Parameters

x (torch.Tensor) – EEG signal representation, the ideal input shape is [n, 32, 128]. Here, n corresponds to the batch size, 32 corresponds to num_electrodes, and 128 corresponds to the number of data points included in the input EEG chunk.

Returns

the predicted probability that the samples belong to the classes.

Return type

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

training: bool