Efficient Labeling of EEG Signal Artifacts Using Active Learning

Electroencephalography (EEG) has been widely used in a variety of contexts, including medical monitoring of subjects as well as performance monitoring in healthy individuals. Recent technological advances have now enabled researchers to quickly record and collect EEG on a wide scale. Although EEG is fairly easy to record, it is highly susceptible to noise sources called artifacts which can occur at amplitudes several times greater than the EEG signal of interest. Because of this, users must manually annotate the EEG signal to identify artifact regions in the data prior to any downstream processing. This can be time-consuming and impractical for large data collections. In this paper we present a method which uses Active Learning (AL) to improve the reliability of existing EEG artifact classifiers with minimal amounts of user interaction. Our results show that classification accuracy equivalent to classifiers trained on full data annotation can be obtained while labeling less than 25% of the data. This suggests significant time savings can be obtained when manually annotating artifacts in large EEG data collections.