SSTEmotionNet

class torcheeg.models.SSTEmotionNet(grid_size: Tuple[int, int] = (32, 32), spectral_in_channels: int = 5, temporal_in_channels: int = 25, spectral_depth: int = 16, temporal_depth: int = 22, spectral_growth_rate: int = 12, temporal_growth_rate: int = 24, num_dense_block: int = 3, hid_channels: int = 50, densenet_dropout: float = 0.0, task_dropout: float = 0.0, num_classes: int = 3)

Spatial-Spectral-Temporal based Attention 3D Dense Network (SST-EmotionNet) for EEG emotion recognition. For more details, please refer to the following information.

• Paper: Jia Z, Lin Y, Cai X, et al. Sst-emotionnet: Spatial-spectral-temporal based attention 3d dense network for eeg emotion recognition[C]//Proceedings of the 28th ACM International Conference on Multimedia. 2020: 2909-2917.

• URL: https://dl.acm.org/doi/abs/10.1145/3394171.3413724

• Related Project: https://github.com/ziyujia/SST-EmotionNet

• Related Project: https://github.com/LexieLiu01/SST-Emotion-Net-Pytorch-Version-

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

dataset = DEAPDataset(io_path=f'./deap',
            root_path='./data_preprocessed_python',
            offline_transform=transforms.Compose([
                transforms.BaselineRemoval(),
                transforms.Concatenate([
                    transforms.Compose([
                        transforms.BandDifferentialEntropy(sampling_rate=128),
                        transforms.MeanStdNormalize()
                    ]),
                    transforms.Compose([
                        transforms.Downsample(num_points=32),
                        transforms.MinMaxNormalize()
                    ])
                ]),
                transforms.ToInterpolatedGrid(DEAP_CHANNEL_LOCATION_DICT)
            ]),
            online_transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Resize((16, 16))
            ]),
            label_transform=transforms.Compose([
                transforms.Select('valence'),
                transforms.Binary(5.0),
            ]))
model = SSTEmotionNet(temporal_in_channels=32, spectral_in_channels=4, grid_size=(16, 16), num_classes=2)

Parameters:

• grid_size (tuple) – Spatial dimensions of grid-like EEG representation. (default: (16, 16))

• spectral_in_channels (int) – How many 2D maps are stacked in the 3D spatial-spectral representation. (default: 5)

• temporal_in_channels (int) – How many 2D maps are stacked in the 3D spatial-temporal representation. (default: 25)

• spectral_depth (int) – The number of layers in spatial-spectral stream. (default: 16)

• temporal_depth (int) – The number of layers in spatial-temporal stream. (default: 22)

• spectral_growth_rate (int) – The growth rate of spatial-spectral stream. (default: 12)

• temporal_growth_rate (int) – The growth rate of spatial-temporal stream. (default: 24)

• num_dense_block (int) – The number of A3DBs to add to end (default: 3)

• hid_channels (int) – The basic hidden channels in the network blocks. (default: 50)

• densenet_dropout (int) – Probability of an element to be zeroed in the dropout layers from densenet blocks. (default: 0.0)

• task_dropout (int) – Probability of an element to be zeroed in the dropout layers from task-specific classification blocks. (default: 0.0)

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

forward(x: Tensor)

Parameters:

x (torch.Tensor) – EEG signal representation, the ideal input shape is [n, 30, 16, 16]. Here, n corresponds to the batch size, 36 corresponds to the sum of spectral_in_channels (e.g., 5) and temporal_in_channels (e.g., 25), and (16, 16) corresponds to grid_size. It is worth noting that the first spectral_in_channels channels should represent spectral information.

Returns:

the predicted probability that the samples belong to the classes.

Return type:

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