This dissertation is a compendium of multiple research papers that, together, address two
main objectives. The first objective and primary research question is to determine
whether or not, through a procedure of independent component analysis (ICA)-based data
mining, volume-domain validation, and source volume estimation, it is possible to
construct a meaningful, objective, and informative model of brain activity from scalpacquired
EEG data. Given that a methodology to construct such a model can be created,
the secondary objective and research question investigated is whether or not the sources
derived from the EEG data can be used to construct a model of complex brain function
associated with the spatial navigation and the virtual Morris Water Task (vMWT).
The assumptions of the signal and noise characteristics of scalp-acquired EEG data were
discussed in the context of what is currently known about functional brain activity to
identify appropriate characteristics by which to separate the activities comprising EEG
data into parts. A new EEG analysis methodology was developed using both synthetic
and real EEG data that encompasses novel algorithms for (1) data-mining of the EEG to
obtain the activities of individual areas of the brain, (2) anatomical modeling of brain
sources that provides information about the 3-dimensional volumes from which each of
the activities separated from the EEG originates, and (3) validation of data mining results
to determine if a source activity found via the data-mining step originates from a distinct
modular unit inside the head or if it is an artefact. The methodology incorporating the
algorithms developed was demonstrated for EEG data collected from study participants
while they navigated a computer-based virtual maze environment. The brain activities of
participants were meaningfully depicted via brain source volume estimation and
representation of the activity relationships of multiple areas of the brain. A case study
was used to demonstrate the analysis methodology as applied to the EEG of an individual
person. In a second study, a group EEG dataset was investigated and activity
relationships between areas of the brain for participants of the group study were
individually depicted to show how brain activities of individuals can be compared to the
group.
The results presented in this dissertation support the conclusion that it is feasible to use
ICA-based data mining to construct a physiological model of coordinated parts of the
brain related to the vMWT from scalp-recorded EEG data. The methodology was
successful in creating an objective and informative model of brain activity from EEG
data. Furthermore, the evidence presented indicates that this methodology can be used to
provide meaningful evaluation of the brain activities of individual persons and to make
comparisons of individual persons against a group.
In sum, the main contributions of this body of work are 5 fold. The technical
contributions are: (1) a new data mining algorithm tailored for EEG, (2) an EEG
component validation algorithm that identifies noise components via their poor
representation in a head model, (3) a volume estimation algorithm that estimates the
region in the brain from which each source waveform found via data mining originates,
(4) a new procedure to study brain activities associated with spatial navigation. The main
contribution of this work to the understanding of brain function is (5) evidence of specific
functional systems within the brain that are used while persons participate in the vMWT
paradigm (Livingstone and Skelton, 2007) examining spatial navigation. / Graduate / 0541 / 0622 / 0623
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/5010 |
| Date | 29 October 2013 |
| Creators | Zeman, Philip Michael |
| Contributors | Skelton, Ronald William, Livingston, Nigel Jonathan |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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