ASSESSING RELIABILITY OF ICA PROJECTIONS - A RESAMPLING APPROACH

When applying unsupervised learning techniques like ICA or temporal decorrelation for BSS, a key question is whether the discovered projections are reliable. In other words: can we give error bars or can we assess the quality of our separation? We use resampling methods to tackle these questions and show experimentally that our proposed variance estimations are strongly correlated to the separation error. We demonstrate that this reliability estimation can be used to choose an appropriate ICA-model, to enhance significantly the separation performance, and, most important, to mark the components that can really have a physical meaning. An application to data from an MEG1-experiment underlines the usefulness of our approach.