xEEGNet

class selfeeg.models.zoo.xEEGNet(nb_classes: int, Chans: int, Samples: int, Fs: int, F1: int = 7, K1: int = 125, F2: int = 7, Pool: int = 75, p: float = 0.2, random_temporal_filter=False, freeze_temporal: int = 1000000000000.0, spatial_depthwise: bool = True, log_activation_base: str = 'dB', norm_type: str = 'batchnorm', global_pooling=True, bias: list[int, int, int] = [False, False, False], dense_hidden: int = -1, return_logits=True, seed=None)[source]

Pytorch implementation of xEEGNet.

For more information see the following paper [xEEG] . The original implementation of EEGconformer can be found here [xEEGgit] . The expected input is a 3D tensor with size:

(Batch x Channels x Samples).

Parameters:

  • nb_classes (int) – The number of classes. If less than 2, a binary classification problem is considered (output dimensions will be [batch, 1] in this case).

  • Chans (int) – The number of EEG channels.

  • Samples (int) – The sample length (number of time steps). It will be used to calculate the embedding size (for head initialization).

  • Fs (int) – The sampling rate of the EEG signal in Hz. It is used to initialize the weights of the filters. Must be specified even if random_temporal_filter is False.

  • F1 (int, optional) –

    The number of output filters in the temporal convolution layer.

    Default = 7

  • K1 (int, optional) –

    The length of the temporal convolutional layer.

    Default = 125

  • F2 (int, optional) –

    The number of output filters in the spatial convolution layer.

    Default = 7

  • Pool (int, optional) –

    Kernel size for temporal pooling.

    Default = 75

  • p (float, optional) –

    Dropout probability in [0,1)

    Default = 0.2

  • random_temporal_filter (bool, optional) –

    If True, initialize the temporal filter weights randomly. Otherwise, use a passband FIR filter.

    Default = False

  • freeze_temporal (int, optional) –

    Number of forward steps to keep the temporal layer frozen.

    Default = 1e12

  • spatial_depthwise (bool, optional) –

    Whether to apply a depthwise layer in the spatial convolution.

    Default = True

  • log_activation_base (str, optional) –

    Base for the logarithmic activation after pooling. Options: “e” (natural log), “10” (logarithm base 10), “dB” (decibel scale).

    Default = “dB”

  • norm_type (str, optional) –

    The type of normalization. Expected values are “batch” or “instance”.

    Default = “batchnorm”

  • global_pooling (bool, optional) –

    If True, apply global average pooling instead of flattening.

    Default = True

  • bias (list[int, int], optional) –

    A 2-element list with boolean values. If the first element is True, a bias will be added to the temporal convolutional layer. If the second element is True, a bias will be added to the spatial convolutional layer. If the third element is True, a bias will be added to the final dense layer.

    Default = [False, False, False]

  • return_logits (bool, optional) –

    If True, return the output as logit. It is suggested to not use False as the pytorch crossentropy loss function applies the softmax internally.

    Default = True

  • seed (int, optional) –

    A custom seed for model initialization. It must be a nonnegative number. If None is passed, no custom seed will be set

    Default = None

References

[xEEG]

zanola et al., xEEGNet: Towards Explainable AI in EEG Dementia Classification. arXiv preprint. 2025. https://doi.org/10.48550/arXiv.2504.21457

Example

>>> import selfeeg.models
>>> import torch
>>> x = torch.randn(4,8,512)
>>> mdl = models.xEEGNet(3, 8, 512, 125)
>>> out = mdl(x)
>>> print(out.shape) # shoud return torch.Size([4, 3])