Safely operating a vehicle requires the full attention of the driver. Should the driver lose focus as a result of performing other tasks simultaneously, there could be disastrous outcomes. To gain insight into a driver’s mental state, the cognitive load experienced by the driver can be investigated. Measuring cognitive load can be done in numerous ways, one popular approach is the use of Electroencephalography (EEG). A lot of the data that can be extracted from EEG-signals, are redundant or irrelevant when trying to classify cognitive load. This thesis focuses on identifying EEG-features relevant to the classification of cognitive load experienced by drivers, through the use of feature selection algorithms. An experimental approach was utilized where three feature selection algorithms (ReliefF, BSS/WSS and BIRS) were applied to the available datasets. The feature subsets produced by the algorithms achieved higher classification accuracies compared to the use of all features. The best performing subset was generated by the ReliefF algorithm which achieved an accuracy of 66%. However, several other unique subsets achieved comparable results, therefore no single feature subset could be identified as most relevant for classification of cognitive load experienced by drivers. To conclude, the proposed approach could not identify features which could be used to confidently predict a driver’s mental state. / Vehicle Driver Monitoring (VDM)

This thesis uses a combination of cognitive-neurophysiological and genetically-sensitive longitudinal designs to study the associations of attention-deficit/hyperactivity disorder (ADHD) with bipolar disorder (BD) and preterm birth, as well as with the risk or protective factors IQ and physical activity. Previous research on preterm-born individuals and individuals with BD suggests ADHD-like symptoms and cognitive impairments, but direct comparisons are limited. Here, we first examine how cortical activity patterns differ between women with adult ADHD and women with BD during rest and task conditions to identify impairments that are specific to or shared between the disorders. The findings provide evidence for commonalities in brain dysfunction between ADHD and BD: frontal theta power may play a role as a marker of neurobiological processes in both disorders. Second, we investigate whether the ADHD-like symptoms and cognitive-neurophysiological impairments seen in preterm-born adolescents are identical to those in ADHD by directly comparing ADHD symptom scores and performance on a cognitive-neurophysiological test battery sensitive to impairments in ADHD across preterm-born adolescents, term-born adolescents with ADHD and term-born controls. We find that ADHD symptoms are increased in the preterm group compared to controls. The analyses further indicate similarities in brain function between ADHD and preterm birth, as well as unique impairments in the preterm group. Taken together, these results suggest that preterm birth may present a risk factor for both ADHD and additional impairments. Third, using twin data we carry out a developmental-genetic analysis of the association between ADHD and IQ, showing that ADHD symptoms and IQ scores significantly predict each other over time. Finally, we explore a putative protective factor for ADHD by investigating the effect of physical activity on ADHD symptoms. Using a population-based sample of twins, we show that physical activity is inversely associated with ADHD symptoms, even after adjusting for unmeasured confounding. Overall, we demonstrate certain commonalities in brain dysfunction between ADHD and BD. Whereas preterm birth and lower IQ present risk factors for ADHD, physical activity emerges as a potential protective factor.

Esta tese apresenta uma revisão relativa às questões cognitivas de processamento de informações envolvidas na aprendizagem motora, para consolidar pesquisa empírica a esse respeito. Baseado em fontes bibliográficas, apresenta-se o contexto histórico da cultura educacional brasileira da pesquisa em movimento humano. Propõe-se metodologia de observação e quantificação de sinais bioelétricos-fisiológicos para identificação de aspectos relacionados a diferentes etapas da aprendizagem humana no âmbito da cognição e da motricidade. Descreve-se experimento dados originais para a área das Ciências do Movimento Humano, em que se monitora – com EEG e EMG – quantifica e interpreta a alteração de sinais de base em relação a modificações ocorridas durante vários momentos da aquisição da memória motora - aprendizagem - relativa à prática deliberada de partitura musical por violonista. Os dados reforçaram as hipóteses já comprovadas na literatura quanto ao maior esforço do sistema nervoso relacionada à exposição do violonista a uma tarefa específica e sua prática deliberada pelo sistema musculoesquelético, não servindo para generalizações, apenas como validação do desenho experimental e das análises estatísticas realizadas. O objetivo de monitorar, quantificar e descrever a dinâmica neural de freqüência eletrofisiológica durante o desenvolvimento de padrões musculoesqueléticos de coordenação e controle, foi alcançado. / This article presents a revision related to the cognitive questions of information processing involved in motor learning, to consolidate empirical research on the subject. The historical Brazilian educational background to culture of the human movement research is presented, based on bibliographical sources. Methodology of observation and quantification of bioelectrical physiological signals is proposed, which serves to identify the modifications occurred during the task-acquisition process. A experiment is described, along with data relevant for the Human Movement Sciences, in which the alteration of base signals in relation to various movements of the task are monitored, quantified and interpreted. The task consists of learning and performing a short musical excerpt by guitarists.

The beauty of affective computing is to make machine more emphatic to the user. Machines with the capability of emotion recognition can actually look inside the user’s head and act according to observed mental state. In this thesis project, we investigate different features set to build an emotion recognition system from electroencephalographic signals. We used pictures from International Affective Picture System to motivate three emotional states: positive valence (pleasant), neutral, negative valence (unpleasant) and also to induce three sets of binary states: positive valence, not positive valence; negative valence, not negative valence; and neutral, not neutral. This experiment was designed with a head cap with six electrodes at the front of the scalp which was used to record data from subjects. To solve the recognition task we developed a system based on Support Vector Machines (SVM) and extracted the features, some of them we got from literature study and some of them proposed by ourselves in order to rate the recognition of emotional states. With this system we were able to achieve an average recognition rate up to 54% for three emotional states and an average recognition rate up to 74% for the binary states, solely based on EEG signals.

Electroencephalogram (EEG) is a well known, and well used method for studying brain activity, and it's possibilities have lately stretched into the car industry, were it's capabilities of detecting sleepiness in drivers are currently being put to the test. When performing EEG signal analysis on the brain, standardized signal bands exists that are characteristic to specific states of mind, such as when a driver is feeling sleepy. However, EEG as a method for studying the brain has major problems. The signal contains a lot of information that can be redundant or irrelevant, and the result is easily influenced and deviant by other parameters, that can cause incorrectness and inaccuracy in the final prediction and classification of the signal frame. One of the important methods for reducing this inaccuracy of EEG, and also reducing the computational cost of the diagnose, is feature selection. Finding key features in the signal, that can support a reliable diagnosis of a specific state of mind, is of great importance. Especially since learning systems, incorrectly predicting or interpreting a signal in the classification stage, can lead to incorrect triggering of safety features in futuristic cars, such as cruiser control. There are many existing feature selection algorithms available, and features that has been tried in different research project. The goal of this research was to help gather more accurate inputs from EEG, through an optimization study, and to increase the reliability of EEG. And by doing so, hopefully improve safety systems in cars, that in turn could help preventing sleepiness-related accidents on roads in the future. This was realized through a study of features, and feature selection algorithms. By determining key features that could distinguish sleepiness from a signal, as well as performing accuracy tests for different feature selection algorithms, the motivation for an optimal selection, based on the used parameters, could be made. However limited this research was, it concluded that Information Gain as a method for selecting features, was the most accurate algorithm, and that some features were better to use then others, such as Huguchi's fractal dimension, and the Hjorth complexity.

This master thesis report describes signal processing parameters of electroencephalography (EEG) signals with a significant difference between the signals from the animal model of clinical depression and the non-depressed animal model. The signal from the depressed model had a weaker power in gamma (30 - 80 Hz) than the non-depressed model during awake and it had a stronger power in delta (1.5 - 4 Hz) during sleep. The report describes the process of using visualisation to understand the shape of the signal which helps with interpreting results and helps with the development of parameters. A generic tool for time-frequency analysis was improved to cope with the size of the weeklong EEG dataset. A method for evaluating the quality of how well the EEG parameters are able to separate the strains with as short recordings as possible was developed. This project shows that it is possible to separate an animal model of depression from an animal model of non-depression based on its EEG and that EEG-classifiers may work as indicative classifiers for depression. Not a lot of data is needed. Further studies are needed to verify that the results are not overly sensitive to recording setup and to study to what extent the results are translational. It might be some of the EEG parameters with significant differences described here are limited to describe the difference between the two strains FSL and SD. But the classifiers have reasonable biological explanations that makes them good candidates for being translational EEG-based classifiers for clinical depression.

Whether ongoing electroencephalogram (EEG) signal contributes to event related potential (ERP) generation is currently a matter of discussion for all sensory modalities. Resolving the controversy between additive and the oscillatory models has become crucial because evoked potentials are increasingly used in clinical practice as a physiological and neuropsychological index of brain areas or as a link with other functional approaches such as fMRI and the underlying network. The key issue is the search for a function underlying these mechanisms. <p><p>Somatosensory evoked potentials are robust indicators of the afferent information at cortical level. In particular, the frontal N30 component of SEP can serve as a reliable physiological index of the dopaminergic motor pathway (Insola et al. 1999, Pierantozzi et al. 1999). Its properties in sensory-motor gating and cognitive processes make its fine analysis particularly interesting. The physiological interpretation and the origin of the frontal N30 are still debated (Allison et al. 1991, Cheron et al. 1994, Karnovsky et al. 1997, Balzamo et al. 2004, Barba et al. 2005).<p><p>In this thesis we have investigated the mechanisms generating the N30 SEP component produced by electrical stimulation at median nerve at wrist, with reference to the current questioning of the additive and oscillatory models of the ERP (Sayers et al. 1974; Basar et al. 1980).<p><p>We have applied analysis of the spectral content of neuronal oscillatory activity recorded in electroencephalographic (EEG) in order to study of dynamic brain processing underlying the N30 component. Concretely for studying whether the occurrence of the N30 related input induce amplitude modulation and/or reorganization of EEG rhythms we have analyzed separately power perturbation and phase synchrony of single EEG oscillations trials by means of event-related spectral perturbation (ERSP) and intertrial coherence (ITC) measurements. In addition, in order to model brain localizations of phase synchrony and power enhancement and to compare them to model localization of the N30 SEP we used swLORETA, a distributive method of source analysis.<p><p>We have demonstrated that:<p>(1) Ongoing EEG signals contribute to the generation of the N30 component (Cheron et al. 2007).<p>(2) Dynamics of ongoing EEG signals underlie the specific behavior of the N30 during gating produced by movement execution (Cebolla et al. 2009).<p>(3) Localization of brain sources generating the N30 SEP component overlaps those generating beta-gamma ongoing oscillations at the same short latency (Cebolla et al. 2010).<p><p>Additionally the work developed during this thesis has served to develop a comprehensive, pragmatic paradigm to identify, evaluate and understand the somatosensory alterations in defined contexts, as illustrated by our recent work on perturbations and adaptations in astronauts over long term microgravity stay. We think that addressing this topic is essential in order to optimize and objectively evaluate adaptation to microgravity. We therefore proposed a detailed project to European Space Agency entitled “The frontal N30 somatosensory evoked potential for the study of sensory-motor and cognitive adaptations in weightlessness: NeuroSEP” (ILSRA 2009) in which we also proposed direct applications for quality of life aboard International Space Station, for the medical field and industry. / Doctorat en Sciences de la motricité / info:eu-repo/semantics/nonPublished

Medical mistakes made during the fatigue state result in the spread of infection, diagnostic error, psychological distress, poor patient outcomes, and ultimately, loss of life. Alarmingly, the fatigue-management systems put forth by government agency have failed to reduce the risks of fatigue in physicians. A shift from “one size fits all” approaches for fatigue management, to individualized fatigue assessment and training, is required. To date, no validated measures of fatigue are feasible for use as portable, on-site assessments. Here, I propose the use new portable EEG technologies recently validated for the collection of ERP data, as a basis for a portable fatigue assessment that is cost effective, portable, and efficient enough to be used in medical professionals. Over the course of three experiments I have provided data to support the use of the MUSE portable EEG headband, in combination with short oddball task to assess fatigue related neural impacts. Results of these experiments indicate that the P300 component is reduced in fatigued subjects in comparison to non-fatigued, and further that there is a strong correlation between subjective fatigue severity and P300 amplitude. / Graduate

Aggression and violence are social behaviors that exact a significant toll on human societies. Individuals with aggressive tendencies display deficits in effortful control, particularly in affectively charged situations. However, not all individuals with poor effortful control are aggressive. This study uses event-related potentials (ERPs) to decompose the chronology of cognitive functions underlying the link between effortful control and aggression. Specifically, this study investigates which ERPs moderate the effortful control - aggression association. We examined three successive ERP components (P2, N2 and P3) for stimuli that required effortful control. Results indicated that N2 activation, but not P2 or P3 activation, moderated the relationship between effortful control and aggression. These effects were present in negative and neutral contexts. This moderating effect was consistent with previous studies linking neural processing efficiency with reduced activation during cognitive control tasks. Our results suggest that efficient cognitive processing moderates the association between effortful control and aggression.

Human cognition involves dynamic neural activities in distributed brain areas. For studying such neural mechanisms, magnetoencephalography (MEG) and electroencephalography (EEG) are two important techniques, as they non-invasively detect neural activities with a high temporal resolution. Recordings by MEG/EEG sensors can be approximated as a linear transformation of the neural activities in the brain space (i.e., the source space). However, we only have a limited number sensors compared with the many possible locations in the brain space; therefore it is challenging to estimate the source neural activities from the sensor recordings, in that we need to solve the underdetermined inverse problem of the linear transformation. Moreover, estimating source activities is typically an intermediate step, whereas the ultimate goal is to understand what information is coded and how information flows in the brain. This requires further statistical analysis of source activities. For example, to study what information is coded in different brain regions and temporal stages, we often regress neural activities on some external covariates; to study dynamic interactions between brain regions, we often quantify the statistical dependence among the activities in those regions through “connectivity” analysis. Traditionally, these analyses are done in two steps: Step 1, solve the linear problem under some regularization or prior assumptions, (e.g., each source location being independent); Step 2, do the regression or connectivity analysis. However, biases induced in the regularization in Step 1 can not be adapted in Step 2 and thus may yield inaccurate regression or connectivity results. To tackle this issue, we present novel one-step methods of regression or connectivity analysis in the source space, where we explicitly modeled the dependence of source activities on the external covariates (in the regression analysis) or the cross-region dependence (in the connectivity analysis), jointly with the source-to-sensor linear transformation. In simulations, we observed better performance by our models than by commonly used two-step approaches, when our model assumptions are reasonably satisfied. Besides the methodological contribution, we also applied our methods in a real MEG/EEG experiment, studying the spatio-temporal neural dynamics in the visual cortex. The human visual cortex is hypothesized to have a hierarchical organization, where low-level regions extract low-level features such as local edges, and high-level regions extract semantic features such as object categories. However, details about the spatio-temporal dynamics are less understood. Here, using both the two-step and our one-step regression models in the source space, we correlated neural responses to naturalistic scene images with the low-level and high-level features extracted from a well-trained convolutional neural network. Additionally, we also studied the interaction between regions along the hierarchy using the two-step and our one-step connectivity models. The results from the two-step and the one-step methods were generally consistent; however, the one-step methods demonstrated some intriguing advantages in the regression analysis, and slightly different patterns in the connectivity analysis. In the consistent results, we not only observed an early-to-late shift from low-level to high-level features, which support feedforward information flow along the hierarchy, but also some novel evidence indicating non-feedforward information flow (e.g., topdown feedback). These results can help us better understand the neural computation in the visual cortex. Finally, we compared the empirical sensitivity between MEG and EEG in this experiment, in detecting dependence between neural responses and visual features. Our results show that the less costly EEG was able to achieve comparable sensitivity with that in MEG when the number of observations was about twice of that in MEG. These results can help researchers empirically choose between MEG and EEG when planning their experiments with limited budgets.