braindecode.modules.PatchTokenizer#

class braindecode.modules.PatchTokenizer(patch_size, n_times, emb_dim=None, learnable=False)[source]#

Tokenize an EEG signal into non-overlapping temporal patches.

Transforms (batch, n_chans, n_times) into (batch, n_chans, n_patches, patch_dim) by splitting the time axis into non-overlapping patches of patch_size samples. This is the shared patch / “tokenization” step used by transformer EEG foundation models (e.g. LaBraM, CBraMod, EEG-DINO).

As in the filter-bank models (FBCNet, FBMSNet), when n_times is not a multiple of patch_size the input is right zero-padded (a warning is emitted at construction); it is never an error.

Two modes:

  • non-learnable (learnable=False, default): a pure reshape, so patch_dim == patch_size and the raw samples of each patch are kept (the patch embedding, if any, lives in the model).

  • learnable (learnable=True): a strided Conv1d (kernel and stride equal to patch_size, applied per channel) maps each patch to emb_dim features, so patch_dim == emb_dim.

Parameters:

  • patch_size (int) – Number of time samples per patch.

  • n_times (int) – Number of time samples of the input, used to set up the right-padding when n_times is not a multiple of patch_size.

  • emb_dim (int, optional) – Output features per patch in learnable mode. Defaults to patch_size. Ignored when learnable=False.

  • learnable (bool, default=False) – Whether the tokenizer is a learned convolution or a fixed reshape.

Examples

>>> import torch
>>> from braindecode.modules import PatchTokenizer
>>> tokenizer = PatchTokenizer(patch_size=200, n_times=1000)
>>> tokenizer(torch.randn(2, 19, 1000)).shape
torch.Size([2, 19, 5, 200])

Methods

forward(x)[source]#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.