UC San Diego

Electroencephalography (EEG) has made much progress since the 1920s, when Dr. Hans Berger began his exploration of what he initially thought of as supernatural psychic energy. Now almost a century later, most laypeople are familiar with the concept of EEG; some may even casually say “we’re on the same wavelength” when in agreement with each other. In the scientific community, rigorous data analysis has protected EEG from dismissal as a pseudoscience. Furthermore, the successful application of independent component analysis (ICA) to EEG data and the subsequent release of EEGLAB, a GUI-based Matlab toolbox for processing EEG data, allows for and facilitates source-level analysis. Today, the lowering costs of data acquisition and storage continue to drive demand for tools and methods to process large datasets. Researchers may default to channel-level analysis, but some types of analysis (e.g. brain connectivity) are more sensible at the source-level. Each chapter in this thesis documents a method of analysis for an EEGLAB STUDY (a grouping of EEG datasets, source-resolved by ICA, for comparative analysis). Chapter 2 shows the EEGLAB plug-in std_envotpo, which creates STUDY-level envelope plots of the topography, and its sub-function statPvaf, which allows for non-parametric statistical analysis of the percent variance accounted for. Chapter 3 explores parameter selection in a new data processing pipeline, which utilizes the Source Information Flow Toolbox (SIFT) and Measure Projection Toolbox (MPT) to calculate brain connectivity estimators at the group level.