Pipelines

Pipeline defines all steps required by an algorithm to obtain predictions. Pipelines are typically a chain of sklearn compatible transformers and end with an 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

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

Base & Utils

utils.create_pipeline_from_config(config)

Create a pipeline from a config file.

utils.FilterBank(estimator[, flatten])

Apply a given indentical pipeline over a bank of filter.