Karch J. A machine learning perspective on repeated measures: Gaussian process panel and person-specific EEG modeling

Humboldt University of Berlin, 2016. — 183 p.Repeated measures obtained from multiple individuals are of crucial importance for developmental research. Examples of repeated measures obtained from multiple individuals include longitudinal panel and electroencephalography (EEG) data. In this thesis, I develop a novel analysis approach based on machine learning methods for each of these two data modalities. For longitudinal panel data, I develop Gaussian process panel modeling (GPPM), which is based on the flexible Bayesian approach of Gaussian process regression. The comparison of GPPM with longitudinal structural equation modeling (SEM), which contains most conventional panel modeling approaches as special cases, reveals that GPPM in turn encompasses longitudinal SEM as a special case. In contrast to longitudinal SEM, GPPM is well suited for continuous-time modeling, can express a larger set of models, and includes a straightforward approach to obtain person-specific predictions. The comparison between the developed GPPM toolbox and existing SEM software reveals that the GPPM representation of popular longitudinal SEMs decreases the amount of time needed for parameter estimation up to ninefold. For EEG data, I develop an approach to derive person-specific models for the identification and quantification of between-person differences in EEG responses that are ignored by conventional EEG analysis methods. The approach relies on a framework that selects the best model for each person based on a large set of hypothesized candidate models using a model selection approach from machine learning. I show how the obtained models can be interpreted on the individual as well as on the group level. I validate the proposed approach on a working memory data set. The results demonstrate that the obtained person-specific models provide a more accurate description of the link between behavior and EEG data than the conventional nonspecific EEG analysis approach.