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Group-Level Analysis of Source-Resolved Event Related Potential and Brain Connectivity


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.

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