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Description
When surgical resection becomes necessary to alleviate a patient's epileptiform activity, that patient is monitored by video synchronized with electrocorticography (ECoG) to determine the type and location of seizure focus. This provides a unique opportunity for researchers to gather neurophysiological data with high temporal and spatial resolution; these data are

When surgical resection becomes necessary to alleviate a patient's epileptiform activity, that patient is monitored by video synchronized with electrocorticography (ECoG) to determine the type and location of seizure focus. This provides a unique opportunity for researchers to gather neurophysiological data with high temporal and spatial resolution; these data are assessed prior to surgical resection to ensure the preservation of the patient's quality of life, e.g. avoid the removal of brain tissue required for speech processing. Currently considered the "gold standard" for the mapping of cortex, electrical cortical stimulation (ECS) involves the systematic activation of pairs of electrodes to localize functionally specific brain regions. This method has distinct limitations, which often includes pain experienced by the patient. Even in the best cases, the technique suffers from subjective assessments on the parts of both patients and physicians, and high inter- and intra-observer variability. Recent advances have been made as researchers have reported the localization of language areas through several signal processing methodologies, all necessitating patient participation in a controlled experiment. The development of a quantification tool to localize speech areas in which a patient is engaged in an unconstrained interpersonal conversation would eliminate the dependence of biased patient and reviewer input, as well as unnecessary discomfort to the patient. Post-hoc ECoG data were gathered from five patients with intractable epilepsy while each was engaged in a conversation with family members or clinicians. After the data were separated into different speech conditions, the power of each was compared to baseline to determine statistically significant activated electrodes. The results of several analytical methods are presented here. The algorithms did not yield language-specific areas exclusively, as broad activation of statistically significant electrodes was apparent across cortical areas. For one patient, 15 adjacent contacts along superior temporal gyrus (STG) and posterior part of the temporal lobe were determined language-significant through a controlled experiment. The task involved a patient lying in bed listening to repeated words, and yielded statistically significant activations that aligned with those of clinical evaluation. The results of this study do not support the hypothesis that unconstrained conversation may be used to localize areas required for receptive and productive speech, yet suggests a simple listening task may be an adequate alternative to direct cortical stimulation.
ContributorsLingo VanGilder, Jennapher (Author) / Helms Tillery, Stephen I (Thesis advisor) / Wahnoun, Remy (Thesis advisor) / Buneo, Christopher (Committee member) / Arizona State University (Publisher)
Created2013
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Description
This research is focused on two separate but related topics. The first uses an electroencephalographic (EEG) brain-computer interface (BCI) to explore the phenomenon of motor learning transfer. The second takes a closer look at the EEG-BCI itself and tests an alternate way of mapping EEG signals into machine commands. We

This research is focused on two separate but related topics. The first uses an electroencephalographic (EEG) brain-computer interface (BCI) to explore the phenomenon of motor learning transfer. The second takes a closer look at the EEG-BCI itself and tests an alternate way of mapping EEG signals into machine commands. We test whether motor learning transfer is more related to use of shared neural structures between imagery and motor execution or to more generalized cognitive factors. Using an EEG-BCI, we train one group of participants to control the movements of a cursor using embodied motor imagery. A second group is trained to control the cursor using abstract motor imagery. A third control group practices moving the cursor using an arm and finger on a touch screen. We hypothesized that if motor learning transfer is related to the use of shared neural structures then the embodied motor imagery group would show more learning transfer than the abstract imaging group. If, on the other hand, motor learning transfer results from more general cognitive processes, then the abstract motor imagery group should also demonstrate motor learning transfer to the manual performance of the same task. Our findings support that motor learning transfer is due to the use of shared neural structures between imaging and motor execution of a task. The abstract group showed no motor learning transfer despite being better at EEG-BCI control than the embodied group. The fact that more participants were able to learn EEG-BCI control using abstract imagery suggests that abstract imagery may be more suitable for EEG-BCIs for some disabilities, while embodied imagery may be more suitable for others. In Part 2, EEG data collected in the above experiment was used to train an artificial neural network (ANN) to map EEG signals to machine commands. We found that our open-source ANN using spectrograms generated from SFFTs is fundamentally different and in some ways superior to Emotiv's proprietary method. Our use of novel combinations of existing technologies along with abstract and embodied imagery facilitates adaptive customization of EEG-BCI control to meet needs of individual users.
Contributorsda Silva, Flavio J. K (Author) / Mcbeath, Michael K (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Presson, Clark (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Development of post-traumatic epilepsy (PTE) after traumatic brain injury (TBI) is a major health concern (5% - 50% of TBI cases). A significant problem in TBI management is the inability to predict which patients will develop PTE. Such prediction, followed by timely treatment, could be highly beneficial to TBI patients.

Development of post-traumatic epilepsy (PTE) after traumatic brain injury (TBI) is a major health concern (5% - 50% of TBI cases). A significant problem in TBI management is the inability to predict which patients will develop PTE. Such prediction, followed by timely treatment, could be highly beneficial to TBI patients. Six male Sprague-Dawley rats were subjected to a controlled cortical impact (CCI). A 6mm piston was pneumatically driven 3mm into the right parietal cortex with velocity of 5.5m/s. The rats were subsequently implanted with 6 intracranial electroencephalographic (EEG) electrodes. Long-term (14-week) continuous EEG recordings were conducted. Using linear (coherence) and non-linear (Lyapunov exponents) measures of EEG dynamics in conjunction with measures of network connectivity, we studied the evolution over time of the functional connectivity between brain sites in order to identify early precursors of development of epilepsy. Four of the six TBI rats developed PTE 6 to 10 weeks after the initial insult to the brain. Analysis of the continuous EEG from these rats showed a gradual increase of the connectivity between critical brain sites in terms of their EEG dynamics, starting at least 2 weeks prior to their first spontaneous seizure. In contrast, for the rats that did not develop epilepsy, connectivity levels did not change, or decreased during the whole course of the experiment across pairs of brain sites. Consistent behavior of functional connectivity changes between brain sites and the "focus" (site of impact) over time was demonstrated for coherence in three out of the four epileptic and in both non-epileptic rats, while for STLmax in all four epileptic and in both non-epileptic rats. This study provided us with the opportunity to quantitatively investigate several aspects of epileptogenesis following traumatic brain injury. Our results strongly support a network pathology that worsens with time. It is conceivable that the observed changes in spatiotemporal dynamics after an initial brain insult, and long before the development of epilepsy, could constitute a basis for predictors of epileptogenesis in TBI patients.
ContributorsTobin, Edward (Author) / Iasemidis, Leonidas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Muthuswamy, Jitendran (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. However, this process still requires that seizures are visually detected and marked by experienced and trained electroencephalographers. The

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. However, this process still requires that seizures are visually detected and marked by experienced and trained electroencephalographers. The motivation for the development of an automated seizure detection algorithm in this research was to assist physicians in such a laborious, time consuming and expensive task. Seizures in the EEG vary in duration (seconds to minutes), morphology and severity (clinical to subclinical, occurrence rate) within the same patient and across patients. The task of seizure detection is also made difficult due to the presence of movement and other recording artifacts. An early approach towards the development of automated seizure detection algorithms utilizing both EEG changes and clinical manifestations resulted to a sensitivity of 70-80% and 1 false detection per hour. Approaches based on artificial neural networks have improved the detection performance at the cost of algorithm's training. Measures of nonlinear dynamics, such as Lyapunov exponents, have been applied successfully to seizure prediction. Within the framework of this MS research, a seizure detection algorithm based on measures of linear and nonlinear dynamics, i.e., the adaptive short-term maximum Lyapunov exponent (ASTLmax) and the adaptive Teager energy (ATE) was developed and tested. The algorithm was tested on long-term (0.5-11.7 days) continuous EEG recordings from five patients (3 with intracranial and 2 with scalp EEG) and a total of 56 seizures, producing a mean sensitivity of 93% and mean specificity of 0.048 false positives per hour. The developed seizure detection algorithm is data-adaptive, training-free and patient-independent. It is expected that this algorithm will assist physicians in reducing the time spent on detecting seizures, lead to faster and more accurate diagnosis, better evaluation of treatment, and possibly to better treatments if it is incorporated on-line and real-time with advanced neuromodulation therapies for epilepsy.
ContributorsVenkataraman, Vinay (Author) / Jassemidis, Leonidas (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2012
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Description
In most of the work using event-related potentials (ERPs), researchers presume the function of specific components based on the careful manipulation of experimental factors, but rarely report direct evidence supporting a relationship between the neural signal and other outcomes. Perhaps most troubling is the lack of evidence that ERPs correlate

In most of the work using event-related potentials (ERPs), researchers presume the function of specific components based on the careful manipulation of experimental factors, but rarely report direct evidence supporting a relationship between the neural signal and other outcomes. Perhaps most troubling is the lack of evidence that ERPs correlate with related behavioral outcomes which should result, at least in part, from the neural processes that ERPs capture. One such example is the NoGo-N2 component, an ERP component elicited in Go/NoGo paradigms. There are two primary theories regarding the functional significance of this component in this context: that the signal represents response inhibition and that the component reflects conflict. In this paper, a trial-level method of analysis for the relationship between ERP component potentials and downstream behavioral outcomes (in this case, response accuracy) using a multi-level modeling framework is proposed to provide discriminatory evidence for one of these theories. Following a description of the research on the NoGo-N2, preliminary data supporting the conflict monitoring theory are presented, noting important limitations. Next, an EEG simulation study is presented in which NoGo-N2 data are generated with a known relationship to fabricated reaction time data, showing that, with added levels of complexity and noise within the data, the MLM approach is consistently successful at extracting the known relationships that occur in real NoGo-N2 data. Next, using independent components analysis (ICA) to extract spatiotemporal components that best represent the signal of interest, a well-powered analysis of the relationship between the NoGo-N2 and response accuracy is used to provide strong discriminatory evidence for the conflict monitoring theory of the NoGo-N2. Finally, implications for the NoGo-N2, as well as all ERP components, are discussed with a focus on how this approach can and should be used. the paper concludes with potential expansions of this approach to areas beyond identifying the function of ERP components.
ContributorsHampton, Ryan Scott (Author) / Varnum, Michael E.W. (Thesis advisor) / Shiota, Michelle N. (Committee member) / Brewer, Gene A. (Committee member) / Blais, Chris (Committee member) / Arizona State University (Publisher)
Created2019
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Description
Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques

Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.
ContributorsDutta, Arindam (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Richmond, Christ (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018
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Description
The advertising industry plays a crucial role in how ideals and norms are established in United States society. Recent work is revealing the negative impact advertisements can have on self-esteem and self-image, especially for women. Unrealistic body-types, often created through photo editing, continue to contribute to eating and emotional

The advertising industry plays a crucial role in how ideals and norms are established in United States society. Recent work is revealing the negative impact advertisements can have on self-esteem and self-image, especially for women. Unrealistic body-types, often created through photo editing, continue to contribute to eating and emotional disorders. Such fabricated ideals hinder the progress of social and economic justice for women. This exploratory study investigates whether images of women in traditionally male-dominated roles can weaken sexist attitudes and whether less sexism and highly sexist groups differ in image processing. Participants who scored high or low on the Ambivalent Sexism Inventory were exposed to a set of images of females in the female-dominated occupation of waitress and females in the male-dominated occupation of construction while measuring their neural activity using EEG. Participants complete the Ambivalent Sexism Inventory before and after the experiment. P3 oddball effects are measured for each participant with the hypothesis that the High Sexism group will view female construction workers with a higher oddball effect than the low sexism group. With 38 participants, there is a significant difference between the groups with individuals scoring low on the ASI showing a greater difference between the waitress and construction worker images compared to individuals scoring high on the ASI. Further, exposure to these images did not significantly reduce ASI scores in either group.
ContributorsOstendorf, Tasha (Author) / Swadener, Elizabeth (Thesis advisor) / Arizona State University (Publisher)
Created2015
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Description
This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional

This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional sources and there is an important need for analytical methods to translate this data into improved learning. Affecting Computing which is the study of new techniques that develop systems to recognize and model human emotions is integrating different physiological signals such as electroencephalogram (EEG) and electromyogram (EMG) to detect and model emotions which later can be used to improve these learning systems.

The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time.

The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques.

The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states.

The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN.

The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments.
ContributorsLujan Moreno, Gustavo A. (Author) / Runger, George C. (Thesis advisor) / Atkinson, Robert K (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Rene (Committee member) / Arizona State University (Publisher)
Created2017
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Description
The research question this thesis aims to answer is whether depressive symptoms of adolescents involved in romantic relationships are related to their rejection sensitivity. It was hypothesized that adolescents who have more rejection sensitivity, indicated by a bigger P3b response, will have more depressive symptoms. This hypothesis was tested by

The research question this thesis aims to answer is whether depressive symptoms of adolescents involved in romantic relationships are related to their rejection sensitivity. It was hypothesized that adolescents who have more rejection sensitivity, indicated by a bigger P3b response, will have more depressive symptoms. This hypothesis was tested by having adolescent couples attend a lab session in which they played a Social Rejection Task while EEG data was being collected. Rejection sensitivity was measured using the activity of the P3b ERP at the Pz electrode. The P3b ERP was chosen to measure rejection sensitivity as it has been used before to measure rejection sensitivity in previous ostracism studies. Depressive symptoms were measured using the 20-item Center for Epidemiological Studies Depression Scale (CES-D, Radloff, 1977). After running a multiple regression analysis, the results did not support the hypothesis; instead, the results showed no relationship between rejection sensitivity and depressive symptoms. The results are also contrary to similar literature which typically shows that the higher the rejection sensitivity, the greater the depressive symptoms.
ContributorsBiera, Alex (Author) / Dishion, Tom (Thesis director) / Ha, Thao (Committee member) / Shore, Danielle (Committee member) / Barrett, The Honors College (Contributor)
Created2015-05
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Description
The experience of language can, as any other experience, change the way that the human brain is organized and connected. Fluency in more than one language should, in turn, change the brain in the same way. Recent research has focused on the differences in processing between bilinguals and monolinguals, and

The experience of language can, as any other experience, change the way that the human brain is organized and connected. Fluency in more than one language should, in turn, change the brain in the same way. Recent research has focused on the differences in processing between bilinguals and monolinguals, and has even ventured into using different neuroimaging techniques to study why these differences exist. What previous research has failed to identify is the mechanism that is responsible for the difference in processing. In an attempt to gather information about these effects, this study explores the possibility that bilingual individuals utilize lower signal strength (and by comparison less biological energy) to complete the same tasks that monolingual individuals do. Using an electroencephalograph (EEG), signal strength is retrieved during two perceptual tasks, the Landolt C and the critical flicker fusion threshold, as well as one executive task (the Stroop task). Most likely due to small sample size, bilingual participants did not perform better than monolingual participants on any of the tasks they were given, but they did show a lower EEG signal strength during the Landolt C task than monolingual participants. Monolingual participants showed a lower EEG signal strength during the Stroop task, which stands to support the idea that a linguistic processing task adds complexity to the bilingual brain. Likewise, analysis revealed a significantly lower signal strength during the critical flicker fusion task for monolingual participants than for bilingual participants. Monolingual participants also had a significantly different variability during the critical flicker fusion threshold task, suggesting that becoming bilingual creates an entirely separate population of individuals. Future research should perform analysis with the addition of a prefrontal cortex electrode to determine if less collaboration during processing is present for bilinguals, and if signal complexity in the prefrontal cortex is lower than other electrodes.
ContributorsMcLees, Sallie (Author) / Náñez Sr., José E (Thesis advisor) / Holloway, Steven (Committee member) / Duran, Nicholas (Committee member) / Arizona State University (Publisher)
Created2019