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.


CONCLUSIONS
CORRMAP helps to efficiently use ICA for the removal EEG artifacts.
MRC Institute of Hearing Research, Southampton, UK.
Filipa Campos
FC
Viola
Jeremy
J
Thorne
Barrie
B
Edmonds
Till
T
Schneider
Tom
T
Eichele
Stefan
S
Debener
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D005133:Eye Movements / Q000502:physiology; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D012984:Software; D012986:Software Validation