This thesis addresses some theoretical issues generated by the results of recent analysis of EEG time series proving the brain dynamics are driven by abrupt changes making them depart from the ordinary Poisson condition. These changes are renewal, unpredictable and non-ergodic. We refer to them as crucial events. How is it possible that this form of randomness be compatible with the generation of waves, for instance alpha waves, whose observation seems to suggest the opposite view the brain is characterized by surprisingly extended coherence? To shed light into this apparently irretrievable contradiction we propose a model based on a generalized form of Langevin equation under the influence of a periodic stimulus. We assume that there exist two different forms of time, a subjective form compatible with Poisson statistical physical and an objective form that is accessible to experimental observation. The transition from the former to the latter form is determined by the brain dynamics interpreted as emerging from the cooperative interaction among many units that, in the absence of cooperation would generate Poisson fluctuations. We call natural time the brain internal time and we make the assumption that in the natural time representation the time evolution of the EEG variable y(t) is determined by a Langevin equation perturbed by a periodic process that in this time representation is hardly distinguishable from an erratic process. We show that the representation of this random process in the experimental time scale is characterized by a surprisingly extended coherence. We show that this model generates a sequence of damped oscillations with a time behavior that is remarkably similar to that derived from the analysis of real EEG's. The main result of this research work is that the existence of crucial events is not incompatible with the alpha wave coherence. In addition to this important result, we find another result that may help our group, or any other research group working on the analysis of brain's dynamics, to prove or to disprove the existence of crucial events. We study the diffusion process generated by fluctuations emerging from the same model after filtering out the alpha coherence, and we study the recursion to the origin. We study the survival probability of this process, namely the probability that up to a given time no re-crossing of the origin occurs. We find that this is an inverse power law with a power that depends on whether or not crucial events exist.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-04-12T11:50:50Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE Silvia Gomes Laurentino ATUALIZADA.pdf: 5003712 bytes, checksum: a1ecd20425707ef3fd5478819c251199 (MD5) / Made available in DSpace on 2016-04-12T11:50:50Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE Silvia Gomes Laurentino ATUALIZADA.pdf: 5003712 bytes, checksum: a1ecd20425707ef3fd5478819c251199 (MD5) Previous issue date: 2015-05-06 / Dentre várias questões comportamentais envolvidas nos transtornos depressivos, os conflitos decisórios se destacam por afetar diretamente a vida social e profissional. Estudos de neuroimagem demonstram que os circuitos neurais envolvidos nos processos decisórios e na emoção social, são os mesmos que se apresentam disfuncionais na depressão. Nosso objetivo foi verificar o processo de tomada de decisão em situação de risco/recompensa e de conflito moral em indivíduos deprimidos através de métodos neuropsicofisiológicos. Métodos: Foram avaliados 40 pacientes, sendo 20 com depressão e 20 sem depressão. Aplicou-se questionários para diagnóstico e gravidade da depressão (SCID-IV e Hamilton) em todos os voluntários. Os indivíduos foram, posteriormente, submetidos as tarefas envolvendo risco-recompensa, o Iowa Gambling Task (IGT), e uma tarefa de conflito moral (dilemas do Trolley e Footbridge). Durante as tarefas foi registrado o EEG com 21 canais, para o estudo do Índice de Assimetria do Alfa Frontal, e do sLORETA para identificação das áreas corticais. Além disso, realizou-se o registro do Skin Conductance Response (SCR) para o estudo do despertar emocional. Resultados: No estudo da tomada de decisão de risco-recompensa (IGT), o grupo deprimido apresentou um Netscore total menor (p<0,003) quando comparado com não deprimidos. Na avaliação do Netscore por blocos (5 blocos de 20 jogadas), os não deprimidos tiveram um escore mais alto nos dois últimos blocos caracterizando uma curva de aprendizado normal. O efeito antecipatório do IGT medido através do SCR revelou uma resposta de aprendizado emocional apenas para os não deprimidos (p <0.0,05). No EEG em repouso se observou através do Índice de Assimetria do Alfa Frontal (IAAF) uma maior ativação hemisférica frontal direita no grupo de deprimidos (p<0,05) quando comparado aos não deprimidos. O estudo de correlação entre o IAAF e a gravidade do quadro depressivo, usando a Escala de Hamilton, revelou uma correlação negativa (p <0,001; -,675). O estudo do sLORETA revelou, no grupo de deprimidos, maior ativação cortical no giro frontal inferior (BA10) e médio (BA 11) à direita na frequência Beta 3, e no cíngulo posterior (BA31) na frequência Alfa 2. Durante a análise do conflito moral, observou-se ativação no córtex cingulado posterior (BA 31) na frequência Alfa 1, ínsula (BA 13) bilateralmente na frequência Alfa 1, córtex cingulado anterior (BA 32) na frequência Beta 3, e giro temporal superior (BA42) na frequência Theta. Conclusões: Os indivíduos com depressão maior apresentam comprometimento na modulação do circuito cognitivo e sócio-emocional. Indivíduos com depressão maior podem apresentar um comprometimento na fluidez cognitiva para avaliar estratégias e planejamentos executivos que envolve riscorecompensa ou conflito moral pessoal. Associar o IAAF e o sLORETA contribuiu para confirmar que não apenas ás áreas corticais, mas também as oscilações das frequências cerebrais e a lateralização hemisférica participam da regulação do comportamento de tomada de decisão em pacientes com depressão maior. / From several behavioral questions involved in the mood disorders, decision conflicts stand out because can affect directly the social and professional capacity. Neuroimage studies demonstrated that the neural circuits involving in the decision process and social emotion have been the same circuits involved in major depression. For this reason our objective was to study the decision making process in risk/reward situation in addition to moral conflict in depressed patients through neuropsychophysiological methods. Methods: We analyzed 40 patients, 20 with depression and 20 without depression. It was applied a depression questionnaire to gravity and clinical diagnosis (SCID-IV and Hamilton’s scale). The volunteers were submit to the moral conflict and risk-reward task (Iowa Gambling Task). An EEG recording was done to measure the Frontal Alpha Asymmetry Index (FAAI) and the sLORETA to identify cortical areas. Furthermore, it was recording the Skin Conductance Response (SCR) to study the emotional arousal response Results: In the risk-reward decision-making task (IGT), the depressed group presented a total Netscore lower (p<0, 03) than no depressed. Analyzing the Netscore by blocks, no depressed patients had a highest score in the last two blocks featuring a normal learning curve. The emotional anticipatory effect measured through the SCR revealed an emotional learning response just for the no depressed group (p< 0,05).The EEG related to the Frontal Alpha Asymmetry Index (FAAI) showed high right frontal activation in the depressed group (p<0, 05) compared with no depressed. The correlation study between FAAI and Hamilton scale showed a negative correlation (p<0,001; -, 675). The sLORETA study revealed in the depressed group more right activation in the lower and middle frontal gyrus (BA 10, 11) in the Beta 3 frequency and in the posterior cingulate gyrus (BA31) in the Alpha 2 frequency. During the moral personal conflict task, more activation was seen in the right posterior cingulate cortex (BA 31) in the Alpha 1 frequency; insular cortex (BA13) bilaterally; right anterior cingulate cortex (BA32) in the Beta 3 frequency and temporal superior gyrus (BA42) in the Theta frequency. Conclusion: People with major depression have a dysfunction in the circuits, which modulate the cognitive, emotional and social behavior. Patients with depression may have an impairment in cognitive fluidity to evaluate strategies for risk-reward or personal moral conflict. Combined FAAI with sLORETA contribute to confirm that not only cortical areas, but also the oscillations of brain frequencies participate in the regulation of the decision-making behavior in patients with major depression.

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The human brain acts as an intelligent sensor by helping in effective signal communication and execution of logical functions and instructions, thus, coordinating all functions of the human body. More importantly, it shows the potential to combine prior knowledge with adaptive learning, thus ensuring constant improvement. These qualities help the brain to interact efficiently with both, the body (brain-body) as well as the environment (brain-environment). This dissertation attempts to apply the brain-body-environment interactions (BBEI) to elevate human existence and enhance our day-to-day experiences. For instance, when one stepped out of the house in the past, one had to carry keys (for unlocking), money (for purchasing), and a phone (for communication). With the advent of smartphones, this scenario changed completely and today, it is often enough to carry just one's smartphone because all the above activities can be performed with a single device. In the future, with advanced research and progress in BBEI interactions, one will be able to perform many activities by dictating it in one's mind without any physical involvement. This dissertation aims to shift the paradigm of existing brain-computer-interfaces from just ‘control' to ‘monitor, control, enhance, and restore' in three main areas - healthcare, transportation safety, and cryptography. In healthcare, measures were developed for understanding brain-body interactions by correlating cerebral autoregulation with brain signals. The variation in estimated blood flow of brain (obtained through EEG) was detected with evoked change in blood pressure, thus, enabling EEG metrics to be used as a first hand screening tool to check impaired cerebral autoregulation. To enhance road safety, distracted drivers' behavior in various multitasking scenarios while driving was identified by significant changes in the time-frequency spectrum of the EEG signals. A distraction metric was calculated to rank the severity of a distraction task that can be used as an intuitive measure for distraction in people - analogous to the Richter scale for earthquakes. In cryptography, brain-environment interactions (BBEI) were qualitatively and quantitatively modeled to obtain cancelable biometrics and cryptographic keys using brain signals. Two different datasets were used to analyze the key generation process and it was observed that neurokeys established for every subject-task combination were unique, consistent, and can be revoked and re-issued in case of a breach. This dissertation envisions a future where humans and technology are intuitively connected by a seamless flow of information through ‘the most intelligent sensor', the brain.

Diagnosing post-traumatic stress disorder (PTSD) is challenging and is currently, diagnosis through self-administered checklists. Because a diagnosis of PTSD can open up significant benefits to compensation, education, and medical care, people can tailor their responses to the checklist to help ensure a diagnosis of PTSD. The purpose of the study was to examine the utility of the quantitative electroencephalograph for diagnosing PTSD. Frequency and presence of biomarkers and alpha brain wave symmetry in the frontal and parietal lobes were examined. Research questions involved examining the presence of alpha wave imbalance across the frontal lobe and between the right and left parietal lobes. A secondary data analysis was conducted using data from 108 subjects; these data included records from those with and without a PTSD diagnosis. The results of logistic regression showed that 63% of the clients diagnosed with PTSD were correctly identified and between 7% and 8% of the variance in PTSD was accounted for by frontal lobe asymmetry. The parietal lobe imbalance correctly classified PTSD in 59% of the patients and it identified 3.5–4.9% of the variance, suggesting that asymmetry in the frontal and parietal lobes should not be used as the primary method for diagnosing PTSD. Implications for social change include identifying an objective diagnostic tool that can potentially decrease the possibility of inaccurate diagnoses based on self-reported symptoms. This could lead to eliminating some of the shame and embarrassment veterans and first responders feel toward seeking help for PTSD.

Vibrations are often experienced in the workplace and may influence performance and executive function. Research has shown that vibrations may have an affect effect on drowsiness and tests related to inhibitory control. Previous work investigating whole body vibrations (WBV) and their effect was evaluated to inform the decisions for this study. WBV effects on cognitive abilities were examined and the different tests used in these studies were identified and compared. Electroencephalogram (EEG) and event related potentials (ERP) were selected to be used to measure inhibitory and cognitive processes. The N2 ERP, which reflects inhibitory control processes, was examined as well as the dominant frequency of the Fourier fast transform (FFT). A total of 94 participants between the ages of 18-55 (Mage = 20.49 SDage = 1.68) completed this study (51 female, 38 male and 5 with no gender listed). A go/no-go task was used to elicit the N2 ERP after WBV and a simultaneous EEG recording while the participants experienced WBV was used to gather the needed data. Stimulus frequencies used for the N2 ERP included 15 Hz, 20 Hz, and 40 Hz. During the simultaneous recording stimulus frequency varied every 30 seconds by 10 Hz from 20 Hz to 110 Hz. Data were analyzed using both a linear mixed effects model for normally distributed data and a generalized linear mixed effects model for data taken as percentages. It was hypothesized that there would be an effect on performance as measured in the raw go/no-go results, that this change in performance showing improved accuracy would be linked to inhibitory control, and be seen as a decrease in the magnitude of the N2 ERP. It was also hypothesized that the exploratory FFT portion of the study would produce a shift from a higher to a lower frequency in the dominant waveform . The results show that there were no main effects in either the behavioral performance or in the N2 ERP of the participants but that there was a significant interaction at 40 Hz with improved simple go trial activity and decreased no-go inhibition. The results also show that there was a statistically significant shift in neural oscillation activity but that this shift was not real-world relevant within the context of this study.

Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

Infants have an innate desire to form social bonds and jealousy protests are observed when an infant is trying to regain attention lost by a caregiver to a social competitor. The current study examined jealousy responses during the first year of life, between 6- to 9-months of age and 12- to 18-months of age, in response to loss of exclusive maternal attention, in addition to exploring implications for mother-infant attachment, EEG asymmetry, and cortisol reactivity and regulation. At both age groups, infants demonstrated increased approach behaviors when infants are faced with a social rival, in addition, left-frontal EEG asymmetry was associated with maternal-directed approach behaviors during the social rival condition. In the 6- to 9-month sample, left-frontal EEG asymmetry also demonstrated an association with infants regulatory abilities, measured by salivary cortisol. This study provides further evidence for the emerging links between social and emotional responses in infancy due to loss of exclusive maternal attention. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

The wake human brain constantly samples perceptual information from the environment and integrates them into existing neuronal networks. Neuronal oscillations have been ascribed a key role in the formation of novel memories. The theta rhythm (3-8 Hz) reflects a central executive mechanism, which integrates novel information, reflected in theta-coupled gamma oscillations (> 30 Hz). Alpha oscillations (8-14 Hz) reflect an attentional gating mechanism, which inhibit task irrelevant neuronal processes. In my dissertation I further scrutinized the oscillatory dynamics of memory formation. Study 1 demonstrated that theta-gamma coupling reflects a specific mechanism for associative memory formation. In study 2, I experimentally entrained memory encoding by visual evoked theta-gamma coupling processes, to underline its functional relevance. In two developmental studies, I found that the theta rhythm indexes explicit learning processes in adults and young children (study 3), and that visually entrained theta oscillations are sensitive to the encoding of novel, unexpected events, already in the first year of life (study 4). Throughout these studies alpha oscillations were not sensitive to memory formation processes, but indicated perceptual (study 1) and semantic (study 3) processes. I propose an integrative framework, suggesting that the alpha rhythm reflects activated semantic representations in the neocortex, while theta-gamma coupling reflects an explicit mnemonic control mechanism, which selects, elaborates and integrates activated representations. Specifically, by squeezing real time events onto a faster, neuronal time scale, theta-gamma coding facilitates neuronal plasticity in medio-temporal networks and advances neuronal processes ahead of real time to emulate and guide future behavior.

Simultaneous EEG-fMRI is a multi-modal neuroimaging technique where hemodynamic activity across the brain is measured at millimeter spatial resolution using functional magnetic resonance imaging (fMRI) while electrical activity at the scalp is measured at millisecond resolution using electroencephalography (EEG). EEG-fMRI is significantly more challenging to collect than either modality on its own, due to electromagnetic coupling and interference between the modalities. The rationale for collecting the two modalities together is that, given the complementary spatial and temporal resolutions of the individual modalities, EEG-fMRI has the potential as a non-invasive neuroimaging technique for recovering the latent source space of neural activity. Inferring this latent source space and fusing these modalities has been a main challenge for realizing the potential of simultaneous EEG-fMRI for human neuroscience. In this thesis we develop a principled and interpretable approach to address this inference problem. We build on the knowledge of the generative processes underlying image/signal formation of each of the modalities, traditionally viewed via linear mappings, and recast this into a framework which we refer to as “neural transcoding”. The idea of neural transcoding is to generate a signal of one neuroimaging modality from another by first decoding it into a latent source space and then encoding it into the other measurement space. We implement this transcoding via deep architectures based on convolutional neural networks. We first develop a basic transcoding architecture and test it on simulated EEG-fMRI data. Evaluation on simulated data enables us to assess the model’s ability to recover a latent source space given known ground truth. We then extend this architecture and add a cycle consistency loss to create a cycle-CNN transcoder and show that it outperforms, in terms of the fidelity of recovered source space, both the basic transcoder as well as traditional source estimation techniques, even when we provide those techniques detailed information about the image generation process. We then assess the performance of the cycle-CNN transcoder on real simultaneous EEG-fMRI datasets including an auditory oddball dataset and a three-choice visual categorization dataset. Without any prior knowledge of either the hemodynamic response function or leadfield matrix, the transcoder is able to exploit the temporal and spatial relationships between the modalities and latent source spaces to learn these mappings. We show, for real EEG-fMRI data, the modalities can be transcoded from one to another, and that the transcoded results for unseen test data, have substantial correlation with the ground truth. In addition, we analyze the source space of the transcoders and observe latent neural dynamics that could not be observed with either modality alone--e.g., millimeter by millisecond dynamics of cortical regions representing motor activation and somatosensory feedback for finger movement. Collectively, this thesis demonstrates how one can incorporate a principled understanding of the generative process underlying biomedical image/signal formation in a deep learning framework to build models that are interpretable and symmetrically combine both modalities. It also potentially enables a new type of low-cost computational neuroimaging -- i.e., generating an ``expensive" fMRI BOLD image from ``low cost" EEG data.