Graduation Semester and Year

2016

Language

English

Document Type

Thesis

Degree Name

Master of Science in Biomedical Engineering

Department

Bioengineering

First Advisor

Hanli Liu

Abstract

The motivation of study is to combine multimodality approach EEG-fNIRS to study relationship between neural activity and hemodynamic response applying information theory concepts. Investigating human brain function can be improved by employing a multimodal neuroimaging approach. Since each neuroimaging modality has its own characteristic features especially in terms of spatial and temporal resolution integrating fNIRS responses with EEG could accurately characterize the neurovascular coupling in cortical regions selected as part of the brain network involved in motor and cognitive behavior. Simultaneous recording of fNIRS-EEG is therefore an important resource for studying aspects of neurovascular coupling and its relationship with pathological brain physiology as well as the mechanisms underlying the Blood Oxygen Level Dependent signal. In addition, multimodality measurements such as fNIRS-EEG have the ability to reveal time-dependent functional networks with higher information content than the individual modality taken by itself. EEG data has been used to assess information transfer between different scalp sites for a number of years by employing measures such as delayed mutual information, Granger causality, transfer entropy and a recent variant conditional mutual information. In this study we applied permutation conditional mutual information methods to determine the directionality and coupling strength between EEG and NIRS using multimodal approach EEG-fNIRS. We observe neural activities drive hemodynamic responses at resting state. Further we applied the tool to calculate the directionality between EEG electrodes and observed posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha), dominated by regions in the visual cortex. Opposite patterns of anterior-to posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network.

Keywords

EEG, fNIRS, PCMI

Disciplines

Biomedical Engineering and Bioengineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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