UC San Diego

The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low-cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly correlated and combine signals from many different sources, biological and non-biological, independent component analysis (ICA) has been shown to isolate the various source generator processes underlying those recordings. While ICA-based methods have been seeing more and more use, EEG researchers are hampered by the additional manual intervention necessary for source-resolved analyses. These issues can be largely mitigated through the automation of several stages of EEG source analysis. To this end, we developed and evaluated the ICLabel classifier, an automated independent component classifier trained on a large dataset with crowdsourced labels. The crowdsourced labels were estimated using the novel crowd labeling (CL) algorithm, crowd labeling latent Dirichlet allocation (CL-LDA), developed here. The ICLabel dataset that was used to train the ICLabel classifier was also made public to aid in future development of IC classifiers. We also evaluated artifact subspace reconstruction (ASR), an algorithm for artifact removal which is applicable both offline and in real-time, and aids both channel-level and source-level analyses. These tools are combined in the Real-time EEG Source-mapping toolbox (REST) to showcase the utility and ease of real-time, source-level analyses once the individual components of an EEG analysis pipeline are automated. Finally we evaluate adaptive mixture ICA (AMICA) and explore its utility for automatic EEG segmentation and nonstationary analysis. All of these tools and methods are open-source and freely available online.