Depressive disorders impose a significant mental health burden on individuals and our society. Among smokers there is a high comorbidity of depression/depressive symptoms (e.g., Glassman et al., 1998). Here the parietal EEG alpha asymmetry was used as a dimensional neuropsychological marker of depressive symptoms (i.e., the more depressed, the higher alpha power in the right vs. left parietal lobe during visuospatial tasks [Henriques & Davidson, 1997; Rabe et al, 2005]). Participants, all of whom were smokers and none of whom were clinically depressed, were randomly assigned to the Bupropion group (n = 30) or Placebo group (n = 80) in this double blind study. EEG data during the performance of a visuospatial task were collected prior to and after 14 days on bupropion or placebo capsules. It was found that bupropion significantly reduced the right parietal alpha power and parietal asymmetry whereas placebo did not. Self-reports on depressive symptoms with the Beck Depression Inventory (BDI) were also collected but they did not change after bupropion treatment, suggesting that EEG measures are more sensitive to subtle/early bupropion’s antidepressant effects. Finally the close investigation of individual differences showed that positive (vs. negative) parietal asymmetry during pretreatment predicted greater benefits from bupropion treatment. The present study sheds light on the antidepressant mechanisms of bupropion and represents a valuable addition to the paucity of research on the effects of bupropion on brain activity with EEG measures in general.

It has been estimated that 15 million individuals around the world experience the ill effects of neural disabilities every year. Neural disabilities can affect motor control, such as Locked in Syndrome or Amyotrophic Lateral Sclerosis, whereas other affect working memory, such as schizophrenia, Alzheimer's and Parkinson's. However, recent research has show that mental rehearsal of physical movement tasks may remain intact following higher centre damage, and as such represents a new opportunity to accessing the motor system and using it to control devices. Brain Computer Interfaces (BCI) captures the brain's electrical activity and translates it into real time electrical outputs, independent of the orthodox output pathways of peripheral nervous system and muscles. Utilising the brain's electrical activity BCI has the potential to significantly enhance the lives of many individuals suffering from neurological disorders. Unfortunately, the electrical activity associated with motor activity in these individuals can be lower than normal, with acute cortical infarcts decreasing the alpha wave oscillations for the affected pericentral sensorimotor areas. This has brought into doubt whether the intensity of brain signals in these individuals can be large enough to be used as a BCI system control signal for biofeedback training. This thesis aims to examine both if alternative EEG signal can be used and if externally applied neuromodulation can facilitate the process.

abstract: 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. / Dissertation/Thesis / Masters Thesis Justice Studies 2015

Neuroimaging techniques can give novel insights into the nature of human cognition. We do not wish only to label patterns of activity as potentially associated with a cognitive process, but also to probe this in detail, so as to better examine how it may inform mechanistic theories of cognition. A possible approach towards this goal is to extend EEG 'brain-computer interface' (BCI) tools - where motor movement intent is classified from brain activity - to also investigate visual cognition experiments. We hypothesised that, building on BCI techniques, information from visual object tasks could be classified from EEG data. This could allow novel experimental designs to probe visual information processing in the brain. This can be tested and falsified by application of machine learning algorithms to EEG data from a visual experiment, and quantified by scoring the accuracy at which trials can be correctly classified. Further, we hypothesise that ICA can be used for source-separation of EEG data to produce putative activity patterns associated with visual process mechanisms. Detailed profiling of these ICA sources could be informative to the nature of visual cognition in a way that is not accessible through other means. While ICA has been used previously in removing 'noise' from EEG data, profiling the relation of common ICA sources to cognitive processing appears less well explored. This can be tested and falsified by using ICA sources as training data for the machine learning, and quantified by scoring the accuracy at which trials can be correctly classified using this data, while also comparing this with the equivalent EEG data. We find that machine learning techniques can classify the presence or absence of visual stimuli at 85% accuracy (0.65 AUC) using a single optimised channel of EEG data, and this improves to 87% (0.7 AUC) using data from an equivalent single ICA source. We identify data from this ICA source at time period around 75-125 ms post-stimuli presentation as greatly more informative in decoding the trial label. The most informative ICA source is located in the central occipital region and typically has prominent 10-12Hz synchrony and a -5 μV ERP dip at around 100ms. This appears to be the best predictor of trial identity in our experiment. With these findings, we then explore further experimental designs to investigate ongoing visual attention and perception, attempting online classification of vision using these techniques and IC sources. We discuss how these relate to standard EEG landmarks such as the N170 and P300, and compare their use. With this thesis, we explore this methodology of quantifying EEG neuroimaging data with machine learning separation and classification and discuss how this can be used to investigate visual cognition. We hope the greater information from EEG analyses with predictive power of each ICA source quantified by machine learning separation and classification and discuss how this can be used to investigate visual cognition. We hope the greater information from EEG analyses with predictive power of each ICA source quantified by machine learning might give insight and constraints for macro level models of visual cognition.

Today smartphones are everywhere and they ease the lives of millions of people every day. However there are people who, because of various reasons, are unable to receive the benefits of these devices because they are not able to interact with a smartphone in the intended way; using their hands. In this thesis we investigate an alternative method for interacting with a smartphone; using a commercially available electroencephalography (EEG) headset. EEG is a technique for measuring and recording brain activity, often through the use of sensors placed along the scalp of the user. We developed a prototype of a brain-computer interface (BCI) for use with android and the Emotiv Insight commercial EEG headset. The prototype allows the user to control an on-screen cursor in one dimension within an android application using the Emotiv Insight. We performed three case studies with one participant in each. The participants had no prior experience with EEG headsets or BCIs. We had them train to use the Emotiv Insight with our BCI prototype. After the training was completed they performed a series of tests in order to measure their ability to control an on-screen cursor in one dimension. Finally the participants filled out a questionnaire regarding their subjective experiences of using the Emotiv Insight. These case studies showed the inadequacies of the Emotiv Insight. All three participants had issues with training and using the headset. These issues are reflected in our tests, where 44 out of 45 attempts at moving the cursor to a specific area resulted in a failure. All participants also reported fatigue and headaches during the case studies. We also concluded that the Emotiv Insight provides a poor user experience because of fatigue in longer sessions and the amount of work needed to train the headset.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-05-08T16:16:15Z No. of bitstreams: 1 CinthiaBeatrizDaFonseca_DISSERT.pdf: 934111 bytes, checksum: df7e61d28053fe7eec3f89202dbdc2c1 (MD5) / Approved for entry into archive by Monica Paiva (monicalpaiva@hotmail.com) on 2017-05-08T16:22:59Z (GMT) No. of bitstreams: 1 CinthiaBeatrizDaFonseca_DISSERT.pdf: 934111 bytes, checksum: df7e61d28053fe7eec3f89202dbdc2c1 (MD5) / Made available in DSpace on 2017-05-08T16:22:59Z (GMT). No. of bitstreams: 1 CinthiaBeatrizDaFonseca_DISSERT.pdf: 934111 bytes, checksum: df7e61d28053fe7eec3f89202dbdc2c1 (MD5) Previous issue date: 2016-02-29 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / Introdu??o: A s?ndrome pr?-menstrual (SPM) ? caracterizada por uma s?rie de sintomas emocionais, f?sicos e fisiol?gicos. Diversas estrat?gias t?m sido utilizadas para minimizar os sintomas causados pela SPM. Uma ferramenta alternativa que tem sendo utilizada para o tratamento de doen?as psicol?gicas ? a respira??o abdominal lenta (RAL), que consiste na diminui??o da taxa respirat?ria a uma velocidade que estimule o barorreflexo. Alguns resultados mostram que essa t?cnica fornece melhora o estresse, ansiedade e humor negativo. Sugerindo que esse m?todo pode ser eficaz para a modula??o de respostas emocionais. Objetivo: Efeito agudo da RAL sobre ansiedade, humor, modula??o auton?mica e atividade cerebral em mulheres com SPM. M?todos: 20 mulheres saud?veis com SPM foram alocadas em dois grupos em uma ordem aleat?ria independente (experimental n= 9 e controle n= 11). O grupo experimental realizou RAL em seis ciclos/minuto e no controle mantiveram sua taxa respirat?ria normal, ambas por 20 minutos. Antes e depois da RAL ou controle foram avaliadas estado de ansiedade, humor, variabilidade da frequ?ncia card?aca (VFC) e eletroencefalografia (EEG) em repouso. Os dados do EEG foram analisados pelo programa sLORETA para localiza??o das regi?es cerebrais as quais sofreram mudan?a. Resultados: A an?lise de covari?ncia evidenciou que n?o houve efeito na ansiedade nem no humor (P>0,05). A ANOVA de dois fatores mista mostrou que houve apenas modifica??es nos ?ndices de VFC, ocorridos durante a RAL com uma diminui??o do HF (P<0,001) e aumento do LF/HF (P<0,000). Al?m disso, o sLORETA n?o evidenciou mudan?as na atividade cerebral. Conclus?o: A RAL n?o melhora ansiedade, humor, sistema nervoso aut?nomo card?aco e atividade cerebral em mulheres com SPM. / Introdu??o: A s?ndrome pr?-menstrual (SPM) ? caracterizada por uma s?rie de sintomas emocionais, f?sicos e fisiol?gicos. Diversas estrat?gias t?m sido utilizadas para minimizar os sintomas causados pela SPM. Uma ferramenta alternativa que tem sendo utilizada para o tratamento de doen?as psicol?gicas ? a respira??o abdominal lenta (RAL), que consiste na diminui??o da taxa respirat?ria a uma velocidade que estimule o barorreflexo. Alguns resultados mostram que essa t?cnica fornece melhora o estresse, ansiedade e humor negativo. Sugerindo que esse m?todo pode ser eficaz para a modula??o de respostas emocionais. Objetivo: Efeito agudo da RAL sobre ansiedade, humor, modula??o auton?mica e atividade cerebral em mulheres com SPM. M?todos: 20 mulheres saud?veis com SPM foram alocadas em dois grupos em uma ordem aleat?ria independente (experimental n= 9 e controle n= 11). O grupo experimental realizou RAL em seis ciclos/minuto e no controle mantiveram sua taxa respirat?ria normal, ambas por 20 minutos. Antes e depois da RAL ou controle foram avaliadas estado de ansiedade, humor, variabilidade da frequ?ncia card?aca (VFC) e eletroencefalografia (EEG) em repouso. Os dados do EEG foram analisados pelo programa sLORETA para localiza??o das regi?es cerebrais as quais sofreram mudan?a. Resultados: A an?lise de covari?ncia evidenciou que n?o houve efeito na ansiedade nem no humor (P>0,05). A ANOVA de dois fatores mista mostrou que houve apenas modifica??es nos ?ndices de VFC, ocorridos durante a RAL com uma diminui??o do HF (P<0,001) e aumento do LF/HF (P<0,000). Al?m disso, o sLORETA n?o evidenciou mudan?as na atividade cerebral. Conclus?o: A RAL n?o melhora ansiedade, humor, sistema nervoso aut?nomo card?aco e atividade cerebral em mulheres com SPM.

abstract: 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. / Dissertation/Thesis / M.S. Bioengineering 2013

abstract: The recent spotlight on concussion has illuminated deficits in the current standard of care with regard to addressing acute and persistent cognitive signs and symptoms of mild brain injury. This stems, in part, from the diffuse nature of the injury, which tends not to produce focal cognitive or behavioral deficits that are easily identified or tracked. Indeed it has been shown that patients with enduring symptoms have difficulty describing their problems; therefore, there is an urgent need for a sensitive measure of brain activity that corresponds with higher order cognitive processing. The development of a neurophysiological metric that maps to clinical resolution would inform decisions about diagnosis and prognosis, including the need for clinical intervention to address cognitive deficits. The literature suggests the need for assessment of concussion under cognitively demanding tasks. Here, a joint behavioral- high-density electroencephalography (EEG) paradigm was employed. This allows for the examination of cortical activity patterns during speech comprehension at various levels of degradation in a sentence verification task, imposing the need for higher-order cognitive processes. Eight participants with concussion listened to true-false sentences produced with either moderately to highly intelligible noise-vocoders. Behavioral data were simultaneously collected. The analysis of cortical activation patterns included 1) the examination of event-related potentials, including latency and source localization, and 2) measures of frequency spectra and associated power. Individual performance patterns were assessed during acute injury and a return visit several months following injury. Results demonstrate a combination of task-related electrophysiology measures correspond to changes in task performance during the course of recovery. Further, a discriminant function analysis suggests EEG measures are more sensitive than behavioral measures in distinguishing between individuals with concussion and healthy controls at both injury and recovery, suggesting the robustness of neurophysiological measures during a cognitively demanding task to both injury and persisting pathophysiology. / Dissertation/Thesis / Ph.D. Speech and Hearing Science 2014

No description available.

Over 4 million avoidable hospital admissions result from medication errors (IMS Insitute for Healthcare Informatics, 2013). Human error accounts for 80% of all medical errors (Palmieri, DeLucia, Peterson, Ott, & Green, 2008). Medication administration is a complex process. It is important to understand the cognitive load (CL) of Registered Nurses (RNs) working in an electronic health record environment to identify the risk factors of medication errors. The purpose of this study is to investigate the factors that influence the CL of RNs during medication administration who are working in an electronic health record environment. Simulated medication administration scenarios with varying degrees of multi-tasking were completed with 30 participants. When RNs multi-task during medication administration their CL increases. Furthermore, RNs who have poor sleep quality cannot process high-level tasks as well as those RNs who report a good sleep quality. Future work can limit EEG lead placement to the frontal channels of the EEG. Furthermore, replication of this study with a larger sample and a broader range of competing tasks is indicated.