Pipelines#

Pipeline defines all steps required by an algorithm to obtain predictions.

Pipelines are typically a chain of sklearn compatible transformers and end with a sklearn compatible estimator.

Pipelines#

features.LogVariance()

LogVariance transformer.

features.FM([freq])

Transformer to scale sampling frequency.

features.ExtendedSSVEPSignal()

Prepare FilterBank SSVEP EEG signal for estimating extended covariances.

features.AugmentedDataset([order, lag])

Dataset augmentation methods in a higher dimensional space.

features.StandardScaler_Epoch()

Function to standardize the X raw data for the DeepLearning Method.

csp.TRCSP([nfilter, metric, log, alpha])

Weighted Tikhonov-regularized CSP as described in Lotte and Guan 2011.

classification.SSVEP_CCA(interval, freqs[, ...])

Classifier based on Canonical Correlation Analysis for SSVEP.

classification.SSVEP_TRCA(interval, freqs[, ...])

Classifier based on the Task-Related Component Analysis method [1]_ for SSVEP.

classification.SSVEP_MsetCCA(freqs[, ...])

Classifier based on MsetCCA for SSVEP.

deep_learning.KerasDeepConvNet(loss[, ...])

Keras implementation of the Deep Convolutional Network as described in [R679315cfbef6-1].

deep_learning.KerasEEGITNet(loss[, ...])

Keras implementation of the EEGITNet as described in [Rf5b2ee1af1ae-1].

deep_learning.KerasEEGNet_8_2(loss[, ...])

Keras implementation of the EEGNet as described in [Rd83becb56589-1].

deep_learning.KerasEEGNeX(loss[, optimizer, ...])

Keras implementation of the EEGNex as described in [R643fa75c3283-1].

deep_learning.KerasEEGTCNet(loss[, ...])

Keras implementation of the EEGTCNet as described in [R89b58824c471-1].

deep_learning.KerasShallowConvNet(loss[, ...])

Keras implementation of the Shallow Convolutional Network as described in [R2ccacb732305-1].

Base & Utils#

utils.create_pipeline_from_config(config)

Create a pipeline from a config file.

utils.FilterBank(estimator[, flatten])

Apply a given identical pipeline over a bank of filter.

utils_deep_model.EEGNet(data, input_layer[, ...])

EEGNet block implementation as described in [R820c2366bc63-1].

utils_deep_model.EEGNet_TC(self, input_layer)

utils_deep_model.TCN_block(input_layer, ...)

Temporal Convolutional Network (TCN), TCN_block from [R2eea69aed7b6-1].

utils_pytorch.BraindecodeDatasetLoader([...])

Class to Load the data from MOABB in a format compatible with braindecode.

utils_pytorch.InputShapeSetterEEG([...])

Sets the input dimension of the PyTorch module to the input dimension of the training data.