Submitted by Cristiane Chim (cristiane.chim@ucpel.edu.br) on 2017-07-17T13:47:32Z No. of bitstreams: 1 Gustavo_Ott.pdf: 10434305 bytes, checksum: bba092ff1742babb456df8013f50778c (MD5) / Made available in DSpace on 2017-07-17T13:47:32Z (GMT). No. of bitstreams: 1 Gustavo_Ott.pdf: 10434305 bytes, checksum: bba092ff1742babb456df8013f50778c (MD5) Previous issue date: 2017-02-17 / The Auditory Steady-State Response (ASSR) is a periodic electrical response from the brain which is generated by a healthy or impaired ear without conduction hearing loss subject. This response is evoked by presenting a subject to a periodically varying continuous acoustic signal, typically a sinusoidally modulated tone. The response consists of a waveform whose constituent discrete frequency components have the same periodicity as that of the acoustic signal and remain constant in amplitude and phase over an infinitely long time period. ASSR has been used to objectively assess hearing thresholds for hearing impairment diagnosis, in contrast to traditional subjective methods such as auditory brain-stem responses and audiometry. The objective of this study is to implement experimental setups that detect simulated ASSRs in single and multiple response recordings in order to asses detector performance in different approaches of signal recording parameters and signal processing techniques. The experimental setups were implemented using the MATLAB environment, in which three test scenarios were also developed: (i) a single channel ASSR detector in which the statistical detection tests F test, phase coherence (PC), magnitude-squared coherence (MSC) and Hotteling’s T 2 circular (T2) were used for comparative performance evaluation; (ii) a single channel ASSR detector in which the traditional sweep-by-sweep (SBS) averaging approach performance was compared to the proposed epoch-by-epoch (EBE) averaging aproach; and (iii) a multiple channel ASSR detector in which three Independent Component Analysis (ICA) algorithms – JADE, SOBI, and WASOBI – were applied for comparative performance evaluation from the reference method, in which ICA was not applied. The performance evaluation method was the Receiver Operating Characteristic (ROC) analysis, in which the Area Under the Curve (AUC) metric was used to estimate the detector’s accuracy levels for ASSR detection. From the results of the first test scenario it was concluded that the T2 and MSC tests presented the best overall performance, specially at lower SNR conditions. Results from scenario 2 indicated that the SBS approach resulted in higher accuracy levels after the transitory period of the AUC curve related to test time duration, while the EBE resulted in the steepest AUC curves for the first seconds of test time duration. From the results of scenario 3 it was not observed significant ASSR’s detection time reduction when ICA algorithms were applied in situations closely related to hearing threshold estimation. / A Resposta Auditiva de Estado Estável (RAEE) é uma resposta elétrica periódica gerada pelo cérebro em pacientes com ouvidos saudáveis. Esta resposta é evocada ao ser apresentado ao paciente um estímulo acústico contínuo que varia periodicamente, tipicamente um tom modulado por um sinal sinusoidal. A resposta é constituída por componentes em frequência que têm a mesma periodicidade do estímulo e permanecem constantes em termos de amplitude e fase por um período de tempo infinitamente longo. As RAEEs têm sido utilizadas para avaliar de forma objetiva os limiares de audição para diagnóstico de perda auditiva, em contraste aos métodos tradicionais subjetivos, como a audiometria. O objetivo deste trabalho é a implementação de estruturas experimentais que detectem RAEEs simuladas em abordagens de captação de canal único e de múltiplos canais a fim de avaliar o desempenho do detector em diferentes abordagens de processamento de sinal. As estruturas experimentais foram implementadas utilizando o ambiente MATLAB, no qual três cenários de teste foram desenvolvidos: (i) um detector de RAEE para canal simples com o qual os desempenhos dos testes estatísticos F, coerência de fase (PC), coerência da magnitude ao quadrado (MSC) e T 2 circular de Hotteling (T2) foram comparados; (ii) um detector de RAEE para canal simples com o qual o desempenho a abordagem de promediação tradicional sweep-a-sweep (SBS) foi comparado com o método proposto época-a-época (EBE); e (iii) um detector de RAEE para múltiplos canais com o qual o desempenho de três algoritmos de análise de componentes independentes (ICA) – JADE, SOBI e WASOBI – foram comparados com a detecção sem o uso de ICA. O método de avaliação do desempenho foi a análise Receiver Operating Characteristic (ROC), no qual a métrica de área sob a curva (AUC) for utilizada para estimar os níveis de acurácia da detecção das RAEEs. A partir dos resultados do cenário 1 concluiu-se que os testes T2 e MSC apresentaram os melhores desempenhos, especialmente para condições de baixa razão sinal-ruído. Resultados do cenário 2 indicaram que a abordagem SBS apresentou maiores níveis de acurácia após o período transitório da curva AUC, enquanto a abordagem EBE resultou em incrementos de acurácia mais abruptos para os primeiros segundos de duração do teste. A partir dos resultados do cenários 3 não foi observada uma redução significativa no tempo de detecção das RAEEs quando o ICA foi aplicado em situações próximas da estimação de limiar auditivo.

Distraction during driving is one of the leading contributors to injury and mortality rates in traffic accidents. The aim of this current thesis was to consider 1) whether oculomotor and electrophysiological metrics could act as markers of cognitive distraction; 2) whether decrements in hazard perception performance caused by secondary cognitive task demand are to some extent due to cognitive load interfering with processes of alerting, orienting, inhibitory control and visual search; 3) what elements of secondary cognitive tasks have the greatest impact on hazard perception performance; and 4) whether the susceptibility of previously identified markers of cognitive distraction are affected by primary task difficulty. Over the course of four Experiments we recorded the effects of secondary cognitive task demand on behavioural, oculomotor and electrophysiological metrics during a variety of low-level and complex visual tasks. Taken together the experiments of this thesis have demonstrated that secondary cognitive task demand interferes with not just one but every component process of hazard perception performance that was examined. Next, this research has demonstrated that measures such as blink rates, saccade peak velocities, the spread of fixations along the horizontal axis as well as reductions in alpha and beta power output may be reliable indicators of secondary cognitive task demand regardless of the type of primary task. Finally we have shown that the co-registration of eye movements, EEG and ERP measures is a viable method with which to study the cognitive processes involved in visual processing within low level and complex visual tasks.

abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2018

Executive function describes high-level cognitive-abilities including planning, decision-making, set switching and response inhibition. Impairments of the executive functions in disease states may be subtle but can greatly reduce the quality of life and independence. The overarching theme of this project was to investigate the network of brain regions that are needed to support executive function. This was undertaken using a two-fold approach: one, to apply network analysis to resting state functional Magnetic Resonance Imaging (rs-fMRI) and Diffusion Tensor Imaging (DTI) data in order to describe how differences in morphometry and connectivity correlate to executive function differences of individuals with Mild Cognitive Impairment (MCI), and two, to describe the brain networks involved in one form of executive function, decision-making under uncertain conditions, in young, healthy individuals. Impaired decision-making can dramatically impact day-to-day functioning and understanding the underlying network of regions that support this task can provide a target for future intervention studies. Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were used in the studies of MCI. Individuals were grouped by their executive abilities. A regions-of-interest approach was used to parcel and label various brain regions and a network of connections was constructed out of these regions. Differences between the networks were then compared between the MCI subjects with good and poor executive functions. Those with high executive abilities showed decreased functional network connectivity and increased structural network connectivity. The second arm of these studies was based an original decision-making paradigm that was used to compare of networks involved in decision-making at times of uncertainty in healthy young individuals using both electroencephalography (EEG) and task-based functional magnetic resonance imaging (fMRI). Overall we found greater network connectivity in the uncertain condition of the task than in the certain condition. This suggests that with increased uncertainty comes increased organized connectivity. Taken together, the results of this study re-iterate the notion that cognition depends upon the efficient communication between a network of brain regions rather than on isolated regions. They also highlight the importance of having a well-defined network of nodes and connections for optimal executive functioning.

Individuals who suffer from amyotrophic lateral sclerosis (ALS) have a loss of motor control and possibly the loss of speech. A brain-computer interface (BCI) provides a means for communication through nonmuscular control. Visual BCIs have shown the highest potential when compared to other modalities; nonetheless, visual attention concepts are largely ignored during the development of BCI paradigms. Additionally, individual performance differences and personal preference are not considered in paradigm development. The traditional method to discover the best paradigm for the individual user is trial and error. Visual attention research and personal preference provide the building blocks and guidelines to develop a successful paradigm. This study is an examination of a BCI-based visual attention assessment in an ALS sample. This assessment takes into account the individual’s visual attention characteristics, performance, and personal preference to select a paradigm. The resulting paradigm is optimized to the individual and then tested online against the traditional row-column paradigm. The optimal paradigm had superior performance and preference scores over row-column. These results show that the BCI needs to be calibrated to individual differences in order to obtain the best paradigm for an end user.

Amyotrophic lateral sclerosis and other neurodegenerative disorders can cause individuals to lose control of their muscles until they are unable to move or communicate. The development of brain-computer interface (BCI) technology has provided these individuals with an alternative method of communication that does not require muscle movement. Recent research has shown the impact psychological factors have on BCI performance and has highlighted the need for further research. Working memory is one psychological factor that could influence BCI performance. The purpose of the present study is to evaluate the relationship between working memory and brain-computer interface performance. The results indicate that both working memory and general intelligence are significant predictors of BCI performance. This suggests that working memory training could be used to improve performance on a BCI task.

The effects of neurodegenerative diseases such as amyotrophic-lateral sclerosis (ALS) eventually render those suffering from the illness unable to communicate, leaving their cognitive function relatively unharmed and causing them to be “locked-in” to their own body. With this primary function compromised there has been an increased need for assistive communication methods such as brain-computer interfaces (BCIs). Unlike several augmentative or alternative communication methods (AACs), BCIs do not require any muscular control, which makes this method ideal for people with ALS. The wealth of BCI research focuses mainly on increasing BCI performance through improving stimulus processing and manipulating paradigms. Recent research has suggested a need for studies focused on harnessing psychological qualities of BCI users, such as motivation, mood, emotion, and depression, in order to increase BCI performance through working with the user. The present studies address important issues related to P300-BCI performance: 1) the impact of mood, emotion, motivation, and depression on BCI performance were examined independently; and 2) pleasant, unpleasant, and neutral emotions were induced in order to determine the influence of emotion on BCI performance. By exploring psychological mechanisms that influence BCI performance, further insight can be gained on the best methods for improving BCI performance and increasing the number of potential BCI users. The results from Study 1 did not reveal a significant relationship between any of the four psychological factors and BCI performance. Since previous research has found a significant impact of motivation and mood on BCI performance, it may be the case that these factors only impact performance for some individuals. As this is the first study to directly investigate the impact of emotion and depression on BCI performance, future research should continue to explore these relationships. The results from Study 2 were inconclusive for the pleasant condition, since it appears the pleasant emotion manipulation was unsuccessful. The findings indicate that unpleasant emotions do not have a significant impact on BCI performance. This result is promising since it indicates that individuals should still be able to use the BCI system to communicate, even when they are experiencing unpleasant emotions. Future research should further explore the impact of pleasant emotions on BCI performance.

Sarizotan 1-[(2R)-3,4-dihydro-2H-chromen-2-yl]-N-[[5-(4-fluorophenyl) pyridin-3-yl]methyl] methenamine, showed an in vivo pharmaco-EEG profile resembling that of methylphenidate which is used in attention deficit/hyperactivity disorder (ADHD). In turn, we tested sarizotan against impulsivity in juvenile rats measuring the choice for large delayed vs. a small immediate reward in a T-maze and obtained encouraging results starting at 0.03 mg/kg (plasma levels of ~11 nM). Results from rats treated neonatally with 6-hydroxydopamine (6-OHDA), also supported anti-ADHD activity although starting at 0.3 mg/kg. However, microdialysis studies revealed that free brain concentration of sarizotan at active doses were below its affinity for 5-HT1A receptors, the assumed primary target. In contrast, electrophysiological experiments in mid-brain Raphé serotonergic cells paralleled by plasma sampling showed that there was ~60 % inhibition of firing rate—indicating significant activation of 5-HT1A receptors—at a plasma concentration of 76 nM. In line with this, we observed that sarizotan concentrations in brain homogenates were similar to total blood levels but over 500 fold higher than free extracellular fluid (ECF) concentrations as measured using brain microdialysis. These data suggest that sarizotan may have potential anti-ADHD effects at low doses free of the previously reported side-effects. Moreover, in this case a classical pharmacokinetic–pharmacodynamic relationship based on free brain concentrations seems to be less appropriate than target engagement pharmacodynamic readouts.

abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Psychology 2019

Recognition memory is thought to rely upon both recollection and familiarity. When people recall an episode from the past it is generally considered to reflect the memory process of recollection. Therefore, if people can successfully recall an item, they should be able to recognize it. However, in cued recall paradigms of memory research, participants sometimes correctly recall a studied target word in the presence of a strong semantic cue but then fail to recognize that word as actually having been studied. This paradox and underlying cognitive processes have been minimally studied by scientists, leaving this phenomenon poorly understood. Extant research has investigated some of the conditions necessary to produce these conditions but not the underlying neural correlates that drive them. The present study builds upon earlier studies using Electroencephalogram (EEG) to investigate the neural processes that underlie recognition failures of successfully recalled words. In the present experiment, participants studied words one at a time, and then later were asked to verbally recall these previously studied words as cued by their semantic associates. Following the participant’s verbal response, their recognition memory was tested for the recalled word. The current study aimed to use physiological measures (EEG) to investigate the explicit and implicit cognitive processes that may be involved in the recognition failure of recalled words. The data indicate that successfully recalled words that are recognized are driven by recollection at recall and a combination of recollection and familiarity at recognition, whereas successfully recalled words that are not recognized are instead driven by semantic priming at recall and at recognition, are driven by negative-going ERP effects reflecting implicit processes such as repetition fluency.