Date of Award

1-1-2015

Document Type

Masters Thesis

Degree Name

M.S.

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering

First Advisor

Jun Zhang, Ph.D.

Second Advisor

Caroline Li

Third Advisor

Ronald DeLyser

Fourth Advisor

Adam Hebb

Fifth Advisor

Sara Hanrahan

Keywords

Broca's area, Electroencephalogram, Granger causality, Machine learning, Parkinson’s disease, Verbal fluency

Abstract

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Abdulaziz Saleh Almalaq

Provenance

Received from ProQuest

File Format

application/pdf

Language

en

File Size

122 p.

Discipline

Electrical Engineering

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