EEGInceptionEncoder

class selfeeg.models.encoders.EEGInceptionEncoder(Chans: int, F1: int = 8, D: int = 2, kernel_size: int = 64, pool: int = 4, dropRate: float = 0.5, ELUalpha: float = 1.0, bias: bool = True, batch_momentum: float = 0.1, max_depth_norm: float = 1.0, seed: int = None)[source]

Pytorch Implementation of the EEGInception Encoder.

See EEGInception for some references. The expected input is a 3D tensor with size (Batch x Channels x Samples).

Parameters:

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

  • F1 (int, optional) –

    The number of filters in the first temporal convolutional layer. Other output filters will be calculated according to the paper specification.

    Default = 8

  • D (int, optional) –

    The depth of the depthwise convolutional layer.

    Default = 2

  • kernel_size (int, optional) –

    The length of the temporal convolutional layer.

    Default = 64

  • pool (int, optional) –

    The temporal pooling kernel size.

    Default = 4

  • dropRate (float, optional) –

    The dropout percentage in range [0,1].

    Default = 0.5

  • ELUalpha (float, optional) –

    The alpha value of the ELU activation function.

    Default = 1

  • bias (bool, optional) –

    If True, adds a learnable bias to the output.

    Default = True

  • batch_momentum (float, optional) –

    The batch normalization momentum.

    Default = 0.9

  • max_depth_norm (float, optional) –

    The maximum norm each filter can have in the depthwise block. If None no constraint will be included.

    Default = 1.

  • 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

Example

>>> import selfeeg.models
>>> import torch
>>> x = torch.randn(4,8,64)
>>> mdl = models.EEGInceptionEncoder(8)
>>> out = mdl(x)
>>> print(out.shape) # shoud return torch.Size([4, 12])
>>> print(torch.isnan(out).sum()) # shoud return 0