University of Macau / Faculty of Health Sciences

O EEG é um exame comumentemente utilizado para diagnóstico de patologias como a epilepsia ou distúrbios do sono. Ele costuma ser apresentado e interpretado por intermédio da avaliação - em geral, visual - da representação da frequência e amplitude da atividade elétrica global do encéfalo ao longo do tempo. A avaliação deste exame pode ir além desta análise do sinal de onda e sua descrição fenomenológica. Outras formas de representação dos dados de EEG são possíveis e investigadas neste trabalho. O objetivo deste foi o desenvolvimento de um aplicativo que permitisse visualizar os dados de EEG e de uma abordagem de coerência de EEG - denominada PDC - em dispositivos móveis. Este programa pretendeu dar mobilidade ao profissional de saúde e servir também como ferramenta de ensino e aprendizagem. Revisões sistemáticas da literatura mostraram a viabilidade de tal desenvolvimento. Há diversas abordagens na literatura, porém, não foi localizado nenhum estudo mais profundo quanto a eficácia das ferramentas disponíveis. Pretendeu-se corrigir tal carência propondo-se uma metodologia de avaliação sistemática com o auxílio de distintos usuários com diferentes níveis de habilidade em análise de EEG. Isto permitirá introduzir critérios objetivos para verificar a viabilidade da ferramenta proposta bem como permitirá estabelecer parâmetros de comparação entre diferentes propostas. / An electroencephalogram (EEG) is a test that measures and records the global electrical activity of brain. It\'s commonly used for diseases diagnosis, such as epilepsy and sleep disorders. It\'s often displayed and interpreted observing - visually, nearly always - the waveform representation of brain electrical activity. That interpretation may go one step further signal waveform analysis and its phenomenological description. Alternative ways are possible and are investigated here. The present work deals with the development of an mobile application to show EEG data and an EEG coherence approach - called PDC. It intends provide a mobility option to healthcare professionals and be used as teaching and learning tool. Systematic reviews have shown the feasibility of such development. There are several approaches to similar applications, in the literature. However, was not found any study on the effectiveness of the available tools. We developed a methodology proposal for the systematic evaluation to fill this gap. It will be performed with the aid of different users with distinct skill levels in EEG analysis. This will introduce objective criteria to verify the proposed tool practicability and establish parameters for comparing different proposals.

This thesis set out to investigate the electrophysiological correlates of maths anxiety (MA). Research has shown that those with high MA (HMA) tend to have poorer accuracy and increased reaction time on maths based tasks and that high maths anxious individuals avoid situations where they might have to use maths. This can impact on their future by restricting their degree or job prospects. Previous research has identified the behavioural cognitive and psychological effects of MA and recently studies have begun to examine the associated underlying mechanisms in the brain. Chapter one outlines the background MA behavioural and measurement research before evaluating the neurophysiological methods used in cognitive neuroscience and the use of electroencephalography (EEG) in chapter two. Chapter three continues by outlining previous research concerning the neurophysiological processing of maths and number before evaluating relevant neurophysiological research concerning MA. Four experimental studies are conducted, exploring the neurophysiological underpinnings of MA research using EEG. Each of these recruits 30 participants and measures of electro-cortical (Event Related Potentials (ERPs), Global Field Power, Frequency etc.) and questionnaire measures are implemented. The first study aimed to identify whether the behavioural effects of MA (poorer accuracy and increased reaction time) are consistent with ERP differences (component amplitude and latency differences) in the brain and to understand why these effects are experienced. This revealed no significant comparisons between ERP components and behavioural responses involving low and high maths anxious individuals, but this may have been due to the lack of an anxious response by using a verification task, rather than requiring calculation. Study two introduces the measurement of gamma activity as a neurophysiological measure of anxiety and threat processing and brings three core areas of anxiety research together: Previous studies outline high anxiety in connection with gamma modulation, also showing gamma band activity is associated with the amygdala and finally, that the amygdala is responsible for the processing of threat perception and anxiety. This research has not been brought together when studying MA. Results produced similar ERP findings to the previous study but the introduction of gamma activity into the research provided the first differences between high and low MA (LMA) groups, showing significantly greater gamma activity levels in HMA individuals. However, this study only used numerically-based tasks, thus the third study implemented a non-numerical condition to act as a control. Study three replicates the findings showing a reduced level of gamma activity in high MA individuals for the non-numerical based task, however, this was also reduced for the simple maths task. It was theorised that it is more likely to be the initial threat perception that represents the anxious response and gamma activity increases. To test this and remove any working memory demands, the fourth study implements the presentation of single digit observation (using single digit numbers and letters). Even though there was no demand on working memory, high maths anxious participants displayed similar levels of gamma activity as low maths anxious individuals during letter observation. However, they had significantly greater levels during the observation of number. Findings suggest that HMA individuals may not only struggle with the processing of maths stimuli, but may have a threat-related response to the simple observation of numerical stimuli. This implies that HMA individuals consistently apply an avoidance technique due to a threat response associated with increased levels of gamma activity. The findings of the each study are finally discussed in terms of their contribution to the neurophysiological underpinnings of MA, the first exploration of this using gamma activity, future research and the extent that number anxiety may act as a precursor or sine qua non to MA.

Impulsivity is a common and multifaceted personality trait that is characterized by the presence of heightened reward sensitivity, novelty seeking, lack of premeditation, and behavioural and emotional inhibition deficits (Leshem, 2016a). These behaviours are often associated with substance abuse, gambling disorders, obesity, abnormal time perception, and other psychological and neurological conditions (Bari & Robbins, 2013; Berlin & Rolls, 2004). Reward processing deficits have also been well documented, with many researchers finding an association between impulsivity and the inclination towards smaller, immediate, rewards over larger, delayed rewards (Petry, 2001). Additionally, a larger reward positivity amplitude – an event-related potential component associated with rewards and expectancy – was found for the immediate rewards, relative to delayed rewards in high impulsivity individuals (Cherniawsky & Holroyd, 2013; B. Schmidt, Holroyd, Debener, & Hewig, 2017). The purpose of this thesis was to replicate and extend previous findings, by having participants complete two tasks: delayed gratification and time estimation. In the time estimation task, participants estimated the length of one second. The first task, a replication, assesses subject’s preference for immediate rewards; moreover, the second task extended previous research and functioned as an additional way of assessing reward processing and examined participant’s ability to estimate time. Abnormal time perception in impulsive individuals is thought to contribute to atypical delay gratification behaviour (Wittmann & Paulus, 2008). Electroencephalography (EEG) was recorded from participants during both tasks. Based on previous research on impulsivity (Cherniawsky & Holroyd, 2013; Coull, Cheng, & Meck, 2011; Holroyd & Krigolson, 2007; B. Schmidt et al., 2017), I predicted that impulsivity would affect performance on the time estimation task (which is novel in its use with impulsivity and EEG), and response times and reward positivity amplitudes on both tasks. Counter to my hypothesis, I found that response times and task performance were not affected by impulsivity levels. I also observed that the reward positivity was mediated by impulsivity in the delayed gratification task, but not in the time estimation tasks, suggesting that the tasks activate different neural pathways for reward processing. My results indicate that impulsivity can influence the amplitude of the reward positivity, but that different neural pathways are associated with distinct tasks. Further investigation into quantifiable measures of impulsivity and their effect on various reward processing tasks needs to be conducted. / Graduate / 2020-04-22

No description available.

O EEG é um exame comumentemente utilizado para diagnóstico de patologias como a epilepsia ou distúrbios do sono. Ele costuma ser apresentado e interpretado por intermédio da avaliação - em geral, visual - da representação da frequência e amplitude da atividade elétrica global do encéfalo ao longo do tempo. A avaliação deste exame pode ir além desta análise do sinal de onda e sua descrição fenomenológica. Outras formas de representação dos dados de EEG são possíveis e investigadas neste trabalho. O objetivo deste foi o desenvolvimento de um aplicativo que permitisse visualizar os dados de EEG e de uma abordagem de coerência de EEG - denominada PDC - em dispositivos móveis. Este programa pretendeu dar mobilidade ao profissional de saúde e servir também como ferramenta de ensino e aprendizagem. Revisões sistemáticas da literatura mostraram a viabilidade de tal desenvolvimento. Há diversas abordagens na literatura, porém, não foi localizado nenhum estudo mais profundo quanto a eficácia das ferramentas disponíveis. Pretendeu-se corrigir tal carência propondo-se uma metodologia de avaliação sistemática com o auxílio de distintos usuários com diferentes níveis de habilidade em análise de EEG. Isto permitirá introduzir critérios objetivos para verificar a viabilidade da ferramenta proposta bem como permitirá estabelecer parâmetros de comparação entre diferentes propostas. / An electroencephalogram (EEG) is a test that measures and records the global electrical activity of brain. It\'s commonly used for diseases diagnosis, such as epilepsy and sleep disorders. It\'s often displayed and interpreted observing - visually, nearly always - the waveform representation of brain electrical activity. That interpretation may go one step further signal waveform analysis and its phenomenological description. Alternative ways are possible and are investigated here. The present work deals with the development of an mobile application to show EEG data and an EEG coherence approach - called PDC. It intends provide a mobility option to healthcare professionals and be used as teaching and learning tool. Systematic reviews have shown the feasibility of such development. There are several approaches to similar applications, in the literature. However, was not found any study on the effectiveness of the available tools. We developed a methodology proposal for the systematic evaluation to fill this gap. It will be performed with the aid of different users with distinct skill levels in EEG analysis. This will introduce objective criteria to verify the proposed tool practicability and establish parameters for comparing different proposals.

A thorough delineation of the extent of processing possible without visual awareness is necessary to elucidate the neural mechanisms of visual awareness. Despite extensive research, it is presently unclear whether invisible stimuli can undergo advanced processing. To introduce existing work on this topic, previous behavioral efforts to investigate the extent of processing possible without visual awareness and the psychophysical methods used to render stimuli invisible, such as visual masking and interocular suppression-based techniques, are discussed. Physiological evidence that provide support for and against the possibility that advanced information processing can occur without visual awareness are addressed. The basics of multivariate pattern classification techniques are outlined. The potential of using multivariate pattern classification analyses in conjunction with neuroimaging in the temporal domain to investigate whether advanced processing can occur without visual awareness is discussed. An original study using electroencephalography (EEG) and pattern classification techniques to investigate the extent of processing possible without visual awareness is outlined. The results of the analyses reveal that a pattern classifier did not extract neural signatures of categorical processing from EEG recordings when participants viewed an image that remained invisible for the duration of its presentation. In contrast, the results from a second experiment reveal that the pattern classifier was able to decode the category of invisible images from the EEG time series when the images would eventually become visible. The results provide support for the idea that under certain circumstances, such as when the depth of interocular suppression is reduced, advanced processing for invisible stimuli can occur. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Doctor of Philosophy (Medicine) / Sleepiness is an important source of morbidity in the community, with potentially catastrophic consequences of occupational or driving injuries or accidents. Although many measures of sleepiness exist, there is no gold standard. The electroencephalograph (EEG) has been studied as an indicator of sleep pressure in the waking organism, or sleep depth. A mathematical model has been developed, relating the observed EEG to interactions between groups of neurons in the cortex and thalamus (Robinson, Rennie, Rowe, O'Connor, & Gordon, 2005; Robinson, Rennie, & Wright, 1997). These interactions are thought to be important in the transition from wake to sleep. Sleepiness is common in obstructive sleep apnea (OSA). The measurement of sleepiness would have great utility in quantifying the disease burden, measuring treatment response, or determining fitness for work or driving. This study will evaluate parameters derived from the EEG mathematical model as a measure of sleepiness. It is divided into the following four parts: 1. Subjects with likely OSA based on symptoms and demographics from an international database were compared with matched non-OSA controls. The OSA group showed deficits in executive function and abnormalities on evoked response potential testing. 2. Outcomes from a cross-sectional study in a sleep-clinic OSA population were aggregated by factor analysis into a five summary variables relevant to sleepiness: subjective sleepiness, mood & anxiety, memory & learning, driving, and executive functioning. 3. EEG mathematical model parameters from wake EEG recordings were related to the five summary outcomes. Executive function correlated with a parameter Z, representing the negative feedback loop between the thalamic reticular nucleus and the thalamocortical relay nuclei. 4. EEG model parameters during first NREM sleep cycle of 8 subjects with regular sleep architecture were studied. Net cortical excitation (parameter X) is predicted to increase across the cycle, while there was, as predicted, a greater inhibitory effect of the thalamic reticular nucleus upon thalamocortical relay cells (parameter Z). In this preliminary assessment, EEG model parameters reflecting thalamocortical interactions are sensitive to prefrontal lobe tasks such as executive function, which are known to be vulnerable to sleep loss and sleepiness, and these parameters also show variation with increasing sleep depth.

Modeling of brain activity is often seen as requiring great computing power. However in the special case of modeling scalp EEG it is possible to adopt a continuum approximation for the cortex, and then to use the techniques of wave physics to describe its consequent large-scale dynamics. The model incorporates the following critical components: two classes of neurons (excitatory and inhibitory), the typical number and strength of connections between these two classes, the corresponding connections within the thalamus and between the thalamus and cortex, the time constants and basic physiology of neurons, and the propagation of activity between neurons. Representing the immense intricacy of brain anatomy and physiology with suitable summary equations and average parameter values has meant that the model is able to capture the essential characteristics of EEG and ERPs, and to do so in a computationally manageable way.

Chris Daniel Geisler. / "November 4, 1960." Issued also as a thesis, M.I.T. Dept. of Electrical Engineering, October 29, 1960. / Bibliography: p. 154-158. / Army Signal Corps Contract DA36-039-sc-78108. Dept. of the Army Task 3-99-20-001 and Project 3-99-00-000.