braindecode.models package

Some predefined network architectures for EEG decoding.

Submodules

braindecode.models.base module

braindecode.models.base.find_optimizer(optimizer_name)[source]
class braindecode.models.base.BaseModel[source]

Bases: object

cuda()[source]

Move underlying model to GPU.

parameters()[source]

Return parameters of underlying torch model.

Returns:parameters
Return type:list of torch tensors
compile(loss, optimizer, extra_monitors=None, cropped=False, iterator_seed=0)[source]

Setup training for this model.

Parameters:
  • loss (function (predictions, targets) -> torch scalar)
  • optimizer (torch.optim.Optimizer or string) – Either supply an optimizer or the name of the class (e.g. ‘adam’)
  • extra_monitors (List of Braindecode monitors, optional) – In case you want to monitor additional values except for loss, misclass and runtime.
  • cropped (bool) – Whether to perform cropped decoding, see cropped decoding tutorial.
  • iterator_seed (int) – Seed to seed the iterator random generator.
fit(train_X, train_y, epochs, batch_size, input_time_length=None, validation_data=None, model_constraint=None, remember_best_column=None, scheduler=None, log_0_epoch=True)[source]

Fit the model using the given training data.

Will set epochs_df variable with a pandas dataframe to the history of the training process.

Parameters:
  • train_X (ndarray) – Training input data
  • train_y (1darray) – Training labels
  • epochs (int) – Number of epochs to train
  • batch_size (int)
  • input_time_length (int, optional) – Super crop size, what temporal size is pushed forward through the network, see cropped decoding tuturial.
  • validation_data ((ndarray, 1darray), optional) – X and y for validation set if wanted
  • model_constraint (object, optional) – You can supply MaxNormDefaultConstraint if wanted.
  • remember_best_column (string, optional) – In case you want to do an early stopping/reset parameters to some “best” epoch, define here the monitored value whose minimum determines the best epoch.
  • scheduler (‘cosine’ or None, optional) – Whether to use cosine annealing (CosineAnnealing).
  • log_0_epoch (bool) – Whether to compute the metrics once before training as well.
Returns:

Underlying braindecode Experiment
Return type:

exp
evaluate(X, y)[source]

Evaluate, i.e., compute metrics on given inputs and targets.

Parameters:
  • X (ndarray) – Input data.
  • y (1darray) – Targets.
Returns:

result – Dictionary with result metrics.
Return type:

dict
predict_classes(X, threshold_for_binary_case=None, individual_crops=False)[source]

Predict the labels for given input data.

Parameters:
  • X (ndarray) – Input data.
  • threshold_for_binary_case (float, optional) – In case of a model with single output, the threshold for assigning, label 0 or 1, e.g. 0.5.
Returns:

pred_labels – Predicted labels per trial, optionally for each crop within trial.
Return type:

1darray or list of 1darrays
predict_outs(X, individual_crops=False)[source]

Predict raw outputs of the network for given input.

Parameters:
  • X (ndarray) – Input data.
  • threshold_for_binary_case (float, optional) – In case of a model with single output, the threshold for assigning, label 0 or 1, e.g. 0.5.
  • individual_crops (bool)
Returns:

outs_per_trial – Network outputs for each trial, optionally for each crop within trial.
Return type:

2darray or list of 2darrays

braindecode.models.deep4 module

class braindecode.models.deep4.Deep4Net(in_chans, n_classes, input_time_length, final_conv_length, n_filters_time=25, n_filters_spat=25, filter_time_length=10, pool_time_length=3, pool_time_stride=3, n_filters_2=50, filter_length_2=10, n_filters_3=100, filter_length_3=10, n_filters_4=200, filter_length_4=10, first_nonlin=<function elu>, first_pool_mode='max', first_pool_nonlin=<function identity>, later_nonlin=<function elu>, later_pool_mode='max', later_pool_nonlin=<function identity>, drop_prob=0.5, double_time_convs=False, split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1, stride_before_pool=False)[source]

Bases: braindecode.models.base.BaseModel

Deep ConvNet model from [1].

References

[1]Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730
create_network()[source]

braindecode.models.eegnet module

class braindecode.models.eegnet.Conv2dWithConstraint(*args, max_norm=1, **kwargs)[source]

Bases: torch.nn.modules.conv.Conv2d

forward(x)[source]
class braindecode.models.eegnet.EEGNetv4(in_chans, n_classes, final_conv_length='auto', input_time_length=None, pool_mode='mean', F1=8, D=2, F2=16, kernel_length=64, third_kernel_size=(8, 4), drop_prob=0.25)[source]

Bases: braindecode.models.base.BaseModel

EEGNet v4 model from [EEGNet4].

Notes

This implementation is not guaranteed to be correct, has not been checked by original authors, only reimplemented from the paper description.

References

[EEGNet4]Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018). EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces. arXiv preprint arXiv:1611.08024.
create_network()[source]
class braindecode.models.eegnet.EEGNet(in_chans, n_classes, final_conv_length='auto', input_time_length=None, pool_mode='max', second_kernel_size=(2, 32), third_kernel_size=(8, 4), drop_prob=0.25)[source]

Bases: object

EEGNet model from [EEGNet].

Notes

This implementation is not guaranteed to be correct, has not been checked by original authors, only reimplemented from the paper description.

References

[EEGNet]Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2016). EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces. arXiv preprint arXiv:1611.08024.
create_network()[source]

braindecode.models.hybrid module

class braindecode.models.hybrid.HybridNet(in_chans, n_classes, input_time_length)[source]

Bases: braindecode.models.base.BaseModel

Wrapper for HybridNetModule

create_network()[source]
class braindecode.models.hybrid.HybridNetModule(in_chans, n_classes, input_time_length)[source]

Bases: torch.nn.modules.module.Module

Hybrid ConvNet model from [3].

Very hardcoded at the moment.

References

[3]Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730
create_network()[source]
forward(x)[source]

braindecode.models.shallow_fbcsp module

class braindecode.models.shallow_fbcsp.ShallowFBCSPNet(in_chans, n_classes, input_time_length=None, n_filters_time=40, filter_time_length=25, n_filters_spat=40, pool_time_length=75, pool_time_stride=15, final_conv_length=30, conv_nonlin=<function square>, pool_mode='mean', pool_nonlin=<function safe_log>, split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1, drop_prob=0.5)[source]

Bases: braindecode.models.base.BaseModel

Shallow ConvNet model from [2].

References

[2]Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730
create_network()[source]

braindecode.models.util module

braindecode.models.util.to_dense_prediction_model(model, axis=(2, 3))[source]

Transform a sequential model with strides to a model that outputs dense predictions by removing the strides and instead inserting dilations. Modifies model in-place.

Parameters:
  • model
  • axis (int or (int,int)) – Axis to transform (in terms of intermediate output axes) can either be 2, 3, or (2,3).

Notes

Does not yet work correctly for average pooling. Prior to version 0.1.7, there had been a bug that could move strides backwards one layer.