MHANet

class torcheeg.models.MHANet(num_electrodes: int = 64, chunk_size: int = 64, num_heads: int = 16, bias: bool = False, num_classes: int = 2)

The MHANet model is based on the paper “MHANet: Multi-scale Hybrid Attention Network for Auditory Attention Detection”. For more details, please refer to the following information.

• Paper: Li L, Fan C, Zhang H, et al. MHANet: Multi-scale Hybrid Attention Network for Auditory Attention Detection[J]. International Joint Conference on Artificial Intelligence, 2025.

• URL: https://arxiv.org/abs/2505.15364

• Related Project: https://github.com/fchest/MHANet

Below is a recommended suite for use in auditory attention detection tasks:

from torcheeg.models import MHANet
from torcheeg.datasets import DTUDataset
from torcheeg import transforms
from torch.utils.data import DataLoader

dataset = DTUDataset(root_path='./DATA_preproc',
                      offline_transform=transforms.Compose([
                          transforms.MinMaxNormalize(axis=-1),
                          transforms.To2d()
                      ]),
                      online_transform=transforms.ToTensor(),
                      label_transform=transforms.Compose([
                          transforms.Select('attended_speaker'),
                          transforms.Lambda(lambda x: x - 1)
                      ]))

model = MHANet(num_electrodes=64,
               chunk_size=64,
               num_heads=16,
               bias=False,
               num_classes=2)

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

Parameters:

• num_electrodes (int) – The number of electrodes. (default: 64)

• chunk_size (int) – The sampling rate of EEG signals. (default: 64)

• num_heads (int) – The number of attention heads. (default: 16)

• bias (bool) – Whether to use bias in convolution layers. (default: False)

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

forward(x: Tensor) → Tensor

Parameters:

x (torch.Tensor) – EEG signal representation, the ideal input shape is [n, 64, 64]. Here, n corresponds to the batch size, the first 64 corresponds to num_electrodes, and the second 64 corresponds to chunk_size.

Returns:

The predicted probability that the samples belong to the classes.

Return type:

torch.Tensor[size of batch, number of classes]