Dissertation/Thesis Abstract

The analysis and visualization of electroencephalography data using a provenance-enabled environment and its applications to visualization
by Anderson, Erik W., Ph.D., The University of Utah, 2011, 128; 3481031
Abstract (Summary)

Electroencephalography (EEG) remains a common data collection technique in the field of Neuroscience. Improvements in EEG devices are often expressed as increases in temporal or spatial resolution. The concrete result of these improvements is an ever-increasing amount of data to be processed. Appropriately analyzing EEG data is a complex process requiring detailed provenance to be recorded at each step of the processing workflow. VisTrails is a provenance-based workflow system adapted to enable the processing and visualization of EEG data. To create a suite of tools amenable to EEG analysis, VisTrails was extended to include functionality for manipulating arrays and matrices, digital signal processing, and conversion utilities for visualization. The resulting tool has proven useful in several studies rooted in neuroscience and has led to several new insights about working memory and cognition.

Working memory has been described as short-term retention of information that is no longer accessible in the environment, and the manipulation of this information for subsequent use in guiding behavior. Working memory is viewed as a cognitive process underlying higher-order cognitive functions. Studies show psychomotor processing speed and accuracy account for considerable variance in neural efficiency. This study compared the relative effects of active and sham 10 Hz repetitive transcranial magnetic stimulation applied to dorsolateral prefrontal cortex on indices of neural efficiency in healthy participants performing a working memory paradigm that models the association between working memory load and task behavior. Previous studies identified a relationship between diminished neural efficiency and impaired working memory across a broad array of clinical disorders. In the present study, the authors predicted there would be a main effect of stimulation group on accuracy and processing speed, hence, neural efficiency. We observed a main effect of stimulation for reaction time without an effect on accuracy; even so, there was a robust effect of stimulation on neural efficiency.

Effectively evaluating visualization techniques is a difficult task often assessed through feedback from user studies and expert evaluations. We present an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG). These measurements are used to compare different visualization techniques in terms of the burden they place on a viewer's cognitive resources. In this work, EEG signals and response times are recorded while users interpret different representations of data distributions. This information is processed to provide insight into the cognitive load imposed on the viewer. This work describes the design of the user study performed, the extraction of cognitive load measures from EEG data, and how those measures are used to quantitatively evaluate the effectiveness of visualizations.

Indexing (document details)
Advisor: Silva, Claudio
Commitee: Freire, Juliana, Gerig, Guido, Johnson, Christopher, Preston, Gilbert
School: The University of Utah
Department: School of Computing
School Location: United States -- Utah
Source: DAI-B 73/02, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Computer science
Keywords: Electroenchephalography data, Provenance, Visualization
Publication Number: 3481031
ISBN: 9781124983691
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