Universal classifier improves artifact removal in EEG signals, indicating efficient analysis capabilities.
We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.
No takes yet. Share an insight, caveat, or question.
Winkler et al. (2011) studied this question.
Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context: