Semi-automatic identification of independent components representing EEG artifact

Objective: Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings. Methods: CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA. Results: For eye-related artifacts, a very high degree of overlap between users (phi > 0.80), and between users and CORRMAP (phi > 0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi < 0.70), and between users and CORRMAP (phi < 0.65). Conclusions: These results demonstrate that CORRMAP provides an efficient, convenient and objective way of clustering independent components. Significance: CORRMAP helps to efficiently use ICA for the removal EEG artifacts. (C) 2009 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.