braindecode.training.TimeSeriesLoss#

class braindecode.training.TimeSeriesLoss(loss_function)[source]#

Compute Loss between timeseries targets and predictions. Assumes predictions are in shape: n_batch size x n_classes x n_predictions (in time) Assumes targets are in shape: n_batch size x n_classes x window_len (in time) If the targets contain NaNs, the NaNs will be masked out and the loss will be only computed for predictions valid corresponding to valid target values.

Methods

forward(preds, targets)[source]#

Forward pass.

Parameters
  • preds (torch.Tensor) – Model’s prediction with shape (batch_size, n_classes, n_times).

  • targets (torch.Tensor) – Target labels with shape (batch_size, n_classes, n_times).

Examples using braindecode.training.TimeSeriesLoss#

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset