EEGConformerEncoder

class selfeeg.models.encoders.EEGConformerEncoder(Chans, F: int = 40, K1: int = 25, Pool: int = 75, stride_pool: int = 15, d_model: int = 40, nlayers: int = 6, nheads: int = 10, dim_feedforward: int = 160, activation_transformer: str = 'gelu', p: float = 0.2, p_transformer: float = 0.5, seed: int = None)[source]

Pytorch implementation of the EEGConformer Encoder.

See EEGConformer 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.

  • F (int, optional) –

    The number of output filters in the temporal convolution layer.

    Default = 40

  • K1 (int, optional) –

    The length of the temporal convolutional layer.

    Default = 25

  • Pool (int, optional) –

    The temporal pooling kernel size.

    Default = 75

  • stride_pool (int, optional) –

    The temporal pooling stride.

    Default = 15

  • d_model (int, optional) –

    The embedding size. It is the number of expected features in the input of the transformer encoder layer.

    Default = 40

  • nlayers (int, optional) –

    The number of transformer encoder layers.

    Default = 6

  • nheads (int, optional) –

    The number of heads in the multi-head attention layers.

    Default = 10

  • dim_feedforward (int, optional) –

    The dimension of the feedforward hidden layer in the transformer encoder.

    Default = 160

  • activation_transformer (str or Callabel, optional) –

    The activation function in the transformer encoder. See the PyTorch TransformerEncoderLayer documentation for accepted inputs.

    Default = “gelu”

  • p (float, optional) –

    Dropout probability in the tokenizer. Must be in [0,1)

    Default= 0.2

  • p_transformer (float, optional) –

    Dropout probability in the transformer encoder. Must be in [0,1)

    Default= 0.5

  • 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(4,8,512)
>>> mdl = models.EEGConformerEncoder(8)
>>> out = mdl(x)
>>> print(out.shape) # shoud return torch.Size([4, 224])
>>> print(torch.isnan(out).sum()) # shoud return 0