AUTOMATIC ARTIFACT REJECTION FOR EEG DATA USING HIGH-ORDER STATISTICS AND INDEPENDENT COMPONENT ANALYSIS
While it is now generally accepted that independent component analysis is a good tool for isolating both artifacts and cognitive related activations in EEG data, there is still little consensus about criteria for automatic rejection of artifactual components and single trials. Here we developed a graphical software to semi-automatically assist experimenter in rejecting independent components and noisy single data trials based on their statistical properties. We used kurtosis to detect peaky activity distributions that are characteristic of some types of artifact and entropy to detect unusual activity patterns. EEG-LAB, a user-friendly graphic interface running under Matlab, allows the user to tune and calibrate the rejection criteria, to accept or override the suggested components and trials labeled for rejection, and to compare the results with other rejection methods.