A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Images For Modeling Brain Activities Ph.D. Dissertation Defense

ABSTRACT: Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that pester a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits, helping people with such problems and even train the brain’s ability to focus, remember and respond under certain conditions.

In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) and their fusion in an attempt to bridge together the advantages of both modalities have been studied. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events. These types of experiments are known as Event Related and they are essentially EEG recordings that contain the brain response under specific experimental conditions.

Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results.

EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from both of them is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented. The HMM modeling approach is used in order to derive a feature-based representation of the EEG signal. In this case, we use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI using CPLS is studied. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of tasks. Extensions on the proposed models are examined along with future research directions and applications.

BIO

Konstantinos(Kostas) Michalopoulos is a PhD candidate in the Computer Engineering department of Wright State University, Dayton, OH under the supervision of Professor Bourbakis. He holds a bachelor’s and Master’s degree in Electronics and Computer Engineering from Technical University of Crete, Greece. His research interests mainly include imaging and modeling of brain activities. He will start his professional career at the University of Rochester, School of Medicine this fall 2014.