Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing. The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG. Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis. A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG. Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes. The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit. An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%). In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.

Physiological signals, electroencephalogram (EEG) and heart rate variability (HRV), are generated by complex self-regulating systems. These signals are extremely inhomogeneous and nonstationary, and fluctuate in an irregular and highly complex manner. These fluctuations are due to underlying dynamics of the system. The synchronous neural activity measured as scalp EEG indicates underlying neural dynamics of the brain. Hence, quantitative EEG analysis has become a very useful tool in interpreting results from physiological experiments. The analysis of HRV provides valuable information to assess the autonomous nervous system (ANS). The HRV can be significantly affected by physiological state changes and many disease states. Hence, HRV analysis is becoming a major experimental and diagnostic tool. In this thesis, we focus on the study of EEG and HRV time series using tools from nonlinear time series analysis with special emphasis on its implications in detecting physiological state changes such as, in diseases like epileptic seizure and schizophrenia, and in altered states of consciousness as in sleep and meditation. The proposed nonlinear techniques are used in discriminating different physiological states from control states. Artifact processing of EEG signal Interferences (artifacts) from various sources unavoidably contaminate EEG recordings. In quantitative analysis, results can differ significantly by these artifacts, which may lead to wrong interpretation of the results. In this part of the thesis, we have devised methods to minimize ocular and muscle artifacts in EEG. The artifact correction methods are based on blind source separation (BSS) techniques such as singular value decomposition (SVD), algorithm for multiple signal extraction (AMUSE), canonical correlation analysis (CCA), information maximization (INFOMAX) independent component analysis (ICA) and joint approximate diagonalization of eigen-matrices (JADE) ICA. We have proposed a method to simulate clean and artifact corrupted EEG data based on the BSS methods. In order to enhance the performance of BSS methods, a technique called wavelet-filtered component inclusion method has been introduced. In addition, second-order statistics (SOS) and higher-order statistics (HOS) based BSS methods have been studied considering less number of EEG channels; and performance comparison of these methods has also been made. We have also addressed the problem of simultaneous correction of ocular and muscle artifacts in EEG recordings using the BSS methods. Irrespective of the BSS methods, the component elimination method has introduced high spectral error in all the bands after reconstruction of clean EEG. However, the wavelet filtered component inclusion method has retained almost all spectral powers of EEG channels in theta, alpha, and beta bands after ocular artifact minimization. When the number of EEG channels is very less, the enhanced CCA (SOS BSS) has given superior artifact minimization results than HOS BSS methods, especially in delta band. The component elimination method is used in muscle artifact minimization, and hence the SVD method cannot be used for this purpose since it leads to large spectral distortion of reconstructed EEG. The AMUSE and CCA methods have given comparable performance in muscle artifact minimization. In addition, the JADE method has introduced less mean spectral error compared to other methods. The CCA method has shown superior performance in simultaneous minimization of ocular and muscle artifacts, and AMUSE and JADE methods have given comparable results. Furthermore, the less computation time of wavelet enhanced SOS BSS methods make them very useful in real clinical environments. Fractal characterization of time series In biomedical signal analysis, fractal dimension (FD) is used as a quantitative measure to estimate complexity of physiological signals. Such analysis helps to study physiological processes of underlying systems. The FD can also be used to study dynamics of transitions between different states of systems like brain and ANS, in various physiological and pathological states. In this part, we have proposed a method to estimate FD of time series, called multiresolution box-counting (MRBC) method. A modification of this method resulted in multiresolution length (MRL) method. The estimation performance of the proposed methods is compared with that of Katz, Sevcik, and Higuchi methods, by simulating mathematically defined fractal signals, and also the computation time is compared between the methods. The MRBC and MRL methods have given comparable performance to that of Higuchi method, in estimating FD of waveforms, with the advantage of less computational time. In addition, various properties of the FD are studied and discussed in connection with classical signal processing concepts such as amplitude, frequency, sampling frequency, effect of noise, band width, correlation, etc. The FD value of signals has increased with number of harmonics, noise variance, band-width, and mid-band frequency, and decreased with degree of correlation in AR signal. An analogy between Katz FD and smoothed Teager energy operator has also been made. Application of fractal analysis to EEG and HRV time series The fluctuation of EEG potentials normally depends upon degree of alertness, and varies in amplitude and frequency. Hence, the EEG is an important clinical tool for studying sleep and sleep related disorders, epileptic seizures, schizophrenia, and meditation. In this part of the thesis, we have used FD which gives signal complexity, and detrended fluctuation analysis (DFA) which gives multiscale exponent of time series to quantify EEG. We have extended the concept of FD to multiscale FD to compute complexity of time series at multiple scales. The main applications of the proposed method are epileptic seizure detection, sleep stage detection, schizophrenia EEG analysis, and analysis of heart rate variability during meditation. For seizure detection, we have used intracranial EEG recordings with seizure-free and seizure intervals. In sleep EEG analysis, whole-night sleep EEG is used and results are compared with the manually scored hypnogram. The schizophrenia symptom is further categorized into positive and negative symptoms and complexity is estimated using FD and DFA. We have also analyzed HRV data of Chi and Kundalini meditation using FD and DFA techniques. In all the applications considered, we have tested for statistical significance of the computed parameters, between the case of interest and corresponding control cases, to discriminate between the physiological states. The ocular artifact has reduced FD while muscle artifact increased FD of EEG. The FD of seizure EEG has shown high value compared to that of seizure-free EEG. In addition, the seizure-free EEG has more DFA exponent-1 than seizure EEG. The value of FD of EEG is decreased with deepening of sleep, wake state having high FD value. The FD of REM state sleep EEG showed value between that of wake and state-1. The DFA exponent-1 has increased with deepening of sleep state, having small value for wake state. The REM state has given exponent-1 value between wake and state-1. The schizophrenia subjects have shown lower FD value than healthy controls in all the EEG channels except the bilateral temporal and occipital regions. The positive symptom sub-group has shown comparatively high FD values than healthy controls as well as overall schizophrenia sample in the bilateral tempero-parietal-occipital region. In addition, the positive symptom sub-group has shown significantly higher regional FD values than negative symptom sub-group especially in right temporal region. The overall schizophrenia samples as well as the positive and negative subgroup have shown least FD values in the bilateral frontal region. The values of DFA exponent-2 have shown significant high value in schizophrenia samples. In addition, the schizophrenia group has shown less DFA exponent-1 in bilateral temporal region than healthy control. The FD, multiscale FD, DFA exponents have shown significant performance in discriminating different physiological states from control states. The FD value of HRV time series during meditation is less compared to pre-meditation state in both Chi and Kundalini meditation. Irrespective of the type of meditation, meditation state has shown significantly high DFA exponent-1 than pre-meditation state, and significantly high DFA exponent-2 in pre-meditation state compared to meditation state. Functional connectivity analysis of brain during meditation In functionally related regions of the brain, even in those regions separated by substantial distances, the EEG fluctuations are synchronous, which is termed as functional connectivity. In this part, a novel application of functional connectivity analysis of brain using graph theoretic approach has been made on the EEG recorded from meditation practitioners. We have used 16 channel EEG data from subjects while performing Raja Yoga meditation. The pre-meditation condition is used as control state, against which meditation state is compared. For finding connectivity between EEG of various channels, we have computed pair-wise linear correlation and mutual information between the EEG channels, to form a connection matrix of size 16x16. Then, various graph parameters, such as average connection density, degree of nodes, characteristic path length, and cluster index, are computed from the connection matrix. The computed parameters are projected on to the scalp to get topographic head maps that give spatial variation of the parameter, and results are compared between meditation and pre-meditation states. The meditation state has shown low average connection density, less characteristic path length, and high average degree in fronto-central and central regions. Furthermore, high cluster index is shown in frontal and central regions than pre-meditation state. The parameters such as complexity, characteristic path length and average connection density are used as features in quadratic discriminant classifier to classify meditation and pre-meditation state, and have given good accuracy performance. Connectivity analysis using mutual information has given high average connection density in meditation state in theta, alpha and beta bands compared to pre-meditation state. The characteristic path length is high in delta, alpha and beta bands in meditation state. In addition, the meditation state has shown high degree and cluster index in theta and beta bands compared to pre-meditation state. Nonlinear dynamical characterization of HRV during meditation The cardiovascular system is influenced by internal dynamics as well as from various external factors, which makes the system more dynamic and nonlinear. In this part of the thesis, a novel application of using HRV data for studying Chi and Kundalini meditation has been made. The HRV time series are embedded into higher dimensional phase-space using Takens’ embedding theorem to reconstruct the attractor. After estimating the minimum embedding dimension to unfold the attractor dynamics, the complexity of the attractor is computed using correlation dimension, Lyapunov exponent, and nonlinearity scores. In all the analyses, the pre-meditation state is used as control state against which meditation state is compared. The statistical significance of the parameters estimated is tested to discriminate meditation state from control state. The HRV time series of both pre-meditation and meditation have shown similar minimum embedding dimensions in both Chi and Kundalini meditation. Irrespective of the type of meditation, the meditation state has shown high correlation dimension, largest Lyapunov exponent, and low nonlinearity score compared to pre-meditation state. Recurrent quantification analysis of HRV during meditation In this part, a novel application of recurrent quantification analysis (RQA) to HRV during meditation is studied. Here, the time series is embedded into a higher dimensional phase-space and Euclidean distance between the embedded vectors is calculated to form a distance matrix. The matrix is converted into binary matrix by applying a suitable threshold, and plotted as image to get recurrence plot. Various parameters are extracted from the recurrence plot such as percent recurrence rate, diagonal parameters (determinism, divergence, entropy, ratio), and vertical or horizontal parameters (laminarity, trapping time, maximal vertical line length). The procedure is applied to HRV data during meditation and pre-meditation (control) to discriminate between the states. The HRV of meditation state has shown more diagonal line structure whereas more black patches are observed in pre-meditation state. In addition, at low embedding dimensions, the meditation state has shown low recurrence rate, high determinism, low divergence, low entropy, high ratio, high laminarity, high trapping time, and less maximal vertical line length compared to pre-meditation state. These RQA parameters have shown superior performance in discriminating meditation state from control state.

For most of the naturally occurring signals, especially biomedical signals, the underlying physical process generating the signal is often not fully known, making it difficult to obtain a parametric model. Therefore, signal processing techniques are used to analyze the signal for non-parametrically characterizing the underlying system from which the signals are produced. Most of the real life systems are nonlinear and time varying, which poses a challenge while characterizing them. Additionally, multiple sensors are used to extract signals from such systems, resulting in multichannel signals which are inherently coupled. In this thesis, we counter this challenge by using Recurrence Plot based techniques for characterizing biomedical systems such as heart or brain, using signals such as heart rate variability (HRV), electroencephalogram(EEG) or functional magnetic resonance imaging (fMRI), respectively, extracted from them. In time series analysis, it is well known that a system can be represented by a trajectory in an N-dimensional state space, which completely represents an instance of the system behavior. Such a system characterization has been done using dynamical invariants such as correlation dimension, Lyapunov exponent etc. Takens has shown that when the state variables of the underlying system are not known, one can obtain a trajectory in ‘phase space’ using only the signals obtained from such a system. The phase space trajectory is topologically equivalent to the state space trajectory. This enables us to characterize the system behavior from only the signals sensed from them. However, estimation of correlation dimension, Lyapunov exponent, etc, are vulnerable to non-stationarities in the signal and require large number of sample points for accurate computation, both of which are important in the case of biomedical signals. Alternatively, a technique called Recurrence Plots (RP) has been proposed, which addresses these concerns, apart from providing additional insights. Measures to characterize RPs of single and two channel data are called Recurrence Quantification Analysis (RQA) and cross RQA (CRQA), respectively. These methods have been applied with a good measure of success in diverse areas. However, they have not been studied extensively in the context of experimental biomedical signals, especially multichannel data. In this thesis, the RP technique and its associated measures are briefly reviewed. Using the computational tools developed for this thesis, RP technique has been applied on select single channel, multichannel and multimodal (i.e. multiple channels derived from different modalities) biomedical signals. Connectivity analysis is demonstrated as post-processing of RP analysis on multichannel signals such as EEG and fMRI. Finally, a novel metric, based on the modification of a CRQA measure is proposed, which shows improved results. For the case of single channel signal, we have considered a large database of HRV signals of 112 subjects recorded for both normal and abnormal (anxiety disorder and depression disorder) subjects, in both supine and standing positions. Existing RQA measures, Recurrence Rate and Determinism, were used to distinguish between normal and abnormal subjects with an accuracy of 58.93%. A new measure, MLV has been introduced, using which a classification accuracy of 98.2% is obtained. Correlation between probabilities of recurrence (CPR) is a CRQA measure used to characterize phase synchronization between two signals. In this work, we demonstrate its utility with application to multimodal and multichannel biomedical signals. First, for the multimodal case, we have computed running CPR (rCPR), a modification proposed by us, which allows dynamic estimation of CPR as a function of time, on multimodal cardiac signals (electrocardiogram and arterial blood pressure) and demonstrated that the method can clearly detect abnormalities (premature ventricular contractions); this has potential applications in cardiac care such as assisted automated diagnosis. Second, for the multichannel case, we have used 16 channel EEG signals recorded under various physiological states such as (i) global epileptic seizure and pre-seizure and (ii) focal epilepsy. CPR was computed pair-wise between the channels and a CPR matrix of all pairs was formed. Contour plot of the CPR matrix was obtained to illustrate synchronization. Statistical analysis of CPR matrix for 16 subjects of global epilepsy showed clear differences between pre-seizure and seizure conditions, and a linear discriminant classifier was used in distinguishing between the two conditions with 100% accuracy. Connectivity analysis of multichannel EEG signals was performed by post-processing of the CPR matrix to understand global network-level characterization of the brain. Brain connectivity using thresholded CPR matrix of multichannel EEG signals showed clear differences in the number and pattern of connections in brain connectivity graph between epileptic seizure and pre-seizure. Corresponding brain headmaps provide meaningful insights about synchronization in the brain in those states. K-means clustering of connectivity parameters of CPR and linear correlation obtained from global epileptic seizure and pre-seizure showed significantly larger cluster centroid distances for CPR as opposed to linear correlation, thereby demonstrating the efficacy of CPR. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value. Connectivity analysis on multichannel fMRI signals was performed using CPR matrix and graph theoretic analysis. Adjacency matrix was obtained from CPR matrices after thresholding it using statistical significance tests. Graph theoretic analysis based on communicability was performed to obtain community structures for awake resting and anesthetic sedation states. Concurrent behavioral data showed memory impairment due to anesthesia. Given the fact that previous studies have implicated the hippocampus in memory function, the CPR results showing the hippocampus within the community in awake state and out of it in anesthesia state, demonstrated the biological plausibility of the CPR results. On the other hand, results from linear correlation were less biologically plausible. In biological systems, highly synchronized and desynchronized systems are of interest rather than moderately synchronized ones. However, CPR is approximately a monotonic function of synchronization and hence can assume values which indicate moderate synchronization. In order to emphasize high synchronization/ desynchronization and de-emphasize moderate synchronization, a new method of Correlation Synchronization Convergence Time (CSCT) is proposed. It is obtained using an iterative procedure involving the evaluation of CPR for successive autocorrelations until CPR converges to a chosen threshold. CSCT was evaluated for 16 channel EEG data and corresponding contour plots and histograms were obtained, which shows better discrimination between synchronized and asynchronized states compared to the conventional CPR. This thesis has demonstrated the efficacy of RP technique and associated measures in characterizing various classes of biomedical signals. The results obtained are corroborated by well known physiological facts, and they provide physiologically meaningful insights into the functioning of the underlying biological systems, with potential diagnostic value in healthcare.

The main objective of this study is to learn about the audiovisual stimulator and to create hardware resolution of stimulation LED glasses and in environment of the program LabView application, which operate this stimulation LED glasses and in the same time create sound of stimulation. Use environment of the program LabView. Application, which is create in environment of the program LabView, enable operate stimulation LED glasses and arrange sound from three source with two different method, which use modern AVS. Application contains a lot of security, informative and agreement components.

This thesis deals with analysis and processing of the Sleep Electroencephalogram (EEG) signals. The scope of this thesis can be split into several areas. The first area is application of the Independent Component Analysis (ICA) method for EEG signal analysis. A model of EEG signal formation is proposed and conditions under which this model is valid are examined. It is shown that ICA can be used to remove non-deterministic artifacts contained in the EEG signals. The second area of interest is analysis of stationarity of the Sleep EEG signal. Methods to identify stationary signal segments and to analyze statistical properties of these stationary segments are presented. The third area of interest focuses on spectral analysis of the Sleep EEG signals. Analyses are performed that shows the processes that form particular parts of EEG signals spectrum. Also, random signals that are an integral part of the EEG signals analysis are performed. The last area of interest focuses on elimination of the transition processes that are caused by the filtering of the short EEG signal segments.

<p>Emerging technologies such as real-time travel information systems and automated vehicles (AVs) have profound impacts on driver decision-making behavior. While they generally have positive impacts by enabling drivers to make more informed decisions or by reducing their driving effort, there are several concerns related to inadequate consideration of cognitive and psychological aspects in their design. In this context, this dissertation analyzes different aspects of driver cognition and psychology that arise from drivers’ interactions with these technologies using physiological data collected in two sets of driving simulator experiments.</p> <p>This research analyzes the latent cognitive and psychological effects of real-time travel information using electroencephalogram (EEG) data measured in the first set of driving simulator experiments. Using insights from the previous analysis, a hybrid route choice modeling framework is proposed that incorporates the impacts of the latent information-induced cognitive and psychological effects along with other explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors) on drivers’ route choice decisions. EEG data is analyzed to extract two latent cognitive variables that capture the driver’s cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. </p> <p>Several safety concerns emerge for the transition of control from the automated driving system to a human driver after the vehicle issues a takeover warning under conditional vehicle automation (SAE Level 3). In this context, this study investigates the impacts of driver’s pre-warning cognitive state on takeover performance (i.e., driving performance while resuming manual control) using EEG data measured in the second set of driving simulator experiments. However, there is no comprehensive metric available in the literature that could be used to benchmark the role of driver’s pre-warning cognitive state on takeover performance, as most existing studies ignore the interdependencies between the associated driving performance indicators by analyzing them independently. This study proposes a novel comprehensive takeover performance metric, Takeover Performance Index (TOPI), that combines multiple driving performance indicators representing different aspects of takeover performance. </p> <p>Acknowledging the practical limitations of EEG data to have real-world applications, this dissertation evaluates the driver’s situational awareness (SA) and mental stress using eye-tracking and heart rate measures, respectively, that can be obtained from in-vehicle driver monitoring systems in real-time. The differences in SA and mental stress over time, their correlations, and their impacts on the TOPI are analyzed to evaluate the efficacy of using eye-tracking and heart rate measures for estimating the overall takeover performance in conditionally AVs.</p> The study findings can assist information service providers and auto manufacturers to incorporate driver cognition and psychology in designing safer real-time information and their delivery systems. They can also aid traffic operators to incorporate cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers’ route choices. Further, the study findings provide valuable insights to design operating and licensing strategies, and regulations for conditionally automated vehicles. They can also assist auto manufacturers in designing integrated in-vehicle driver monitoring and warning systems that enhance road safety and user experience.

Les émotions des êtres humains changent régulièrement et parfois de manière brusque entrainant un changement de l’état mental c’est-à-dire de l’aptitude cérébrale à fonctionner normalement. Il en résulte une capacité cognitive (ou état cognitif) de l’individu à pouvoir raisonner, accéder à la mémoire, ou effectuer des déductions, variable selon l’état mental. Ceci affecte, en conséquence, les performances des utilisateurs qui varient en fonction de leurs état cognitifs. Cette thèse vise à optimiser l’état cognitif d’un utilisateur lors de ses interactions avec un environnement virtuel. Comme cet état dépend des émotions, l’optimisation de l’état cognitif peut être réalisée à travers l’optimisation des émotions et en particulier la réduction des émotions négatives. Une première partie concerne les moyens de mesurer en temps réel (par un Module de mesures) l’état émotionnel et mental d’un utilisateur lors de ses interactions avec un environnement virtuel. Nous avons réalisé pour cela quatre études expérimentales avec quatre environnements différents. Nous avons montré que ces mesures peuvent être réalisées en utilisant différents capteurs physiologiques. Nous avons aussi montré qu’il est possible de prédire la tendance de l’excitation (un état mental) à partir d’un traceur de regard. Dans une deuxième partie, nous présentons l’Agent Neural qui modifie les environnements virtuels afin de provoquer une modification de l’état émotionnel d’un utilisateur pour améliorer son état cognitif. Nous avons réalisé quatre études expérimentales avec quatre environnements virtuels, où l’Agent Neural intervient dans ces environnements afin de changer l’état émotionnel de l’utilisateur. Nous avons montré que l’agent est capable d’intervenir dans plusieurs types d’environnements et de modifier les émotions de l’utilisateur. Dans une troisième partie, présentons l’Agent Limbique, qui personnalise et améliore les adaptations faites par l’Agent Neural à travers l’observation et l’apprentissage des impacts des changements des environnements virtuels et des réactions émotionnelles des utilisateurs. Nous avons montré que cet agent est capable d’analyser les interventions de l’Agent Neural et de les modifier. Nous avons montré aussi que l’Agent Limbique est capable de générer une nouvelle règle d’intervention et de prédire son impact sur l’utilisateur. La combinaison du Module de mesures, de l’Agent Neural, et de l’Agent Limbique, nous a permis de créer un système de contrôle cognitif intelligent que nous avons appelé Système Limbique Digital. / The human’s emotions change regularly and sometimes suddenly leading to changes in their mental state which is the brain’s ability to function normally. This mental state’s changes affect the users’ cognitive ability (or cognitive state) to reason, access memory, or make inferences, which varies depending on the mental state. Consequently, this affects the users’ performances which varies according to their cognitive states. This thesis aims to optimize the users’ cognitive state during their interactions with a virtual environment. Since this state depends on emotions, optimization of cognitive state can be achieved through the optimization of emotions and in particular the reduction of negative emotions. In a first part, we present the means of measuring in real time (using a Measuring module) the users’ emotional and mental state during their interactions with a virtual environment. We performed four experimental studies with four different environments. We have shown that these measurements can be performed using different physiological sensors. We have also shown that it is possible to predict the tendency of excitement (a mental state) using an eye tracker. In a second part, we present the Neural Agent which modifies virtual environments to provoke a modification on the users’ emotional state in order to improve their cognitive state. We performed four experimental studies with four virtual environments, in which the Neural Agent intervenes in these environments to change the users’ emotional state. We have shown that the agent is able to intervene in several types of environments and able to modify the users’ emotions. In a third part, we present the Limbic Agent, which personalizes and improves the adaptations performed by the Neural Agent through the observation and the learning from the virtual environments changes’ impacts and the users’ emotional reactions. We have shown that this agent is able to analyze the Neural Agent’s interventions and able to modify them. We have also shown that the Limbic Agent is able to generate a new intervention rule and predict its impact on the user. The combination of the Measuring Module, the Neural Agent, and the Limbic Agent, allowed us to create an intelligent cognitive control system that we called the Digital Limbic System.

Tato práce se zabývá vylepšením algoritmu pro sdružování (clustering) ERP signálů pomocí analýzy časových a prostorových vlastností pseudo-signálů získaných za pomocí metody analýzy nezávislých komponent (Independent Component Analysis). Naším zájmem je nalezení nových vlastností, které by zlepšily stávající výsledky. Tato práce se zabývá použitím Fourierovy transformace (Fourier Transform), FIR filtru a krátkodobé Fourierovy transformace ke zkvalitnění informace pro sdružovací algoritmy. Princip a použitelnost metody jsou popsány a demonstrovány ukázkovým algoritmem. Výsledky ukázaly, že pomocí dané metody je možné získat ze vstupních dat zajímavé informace, které mohou být úspěšně použity ke zlepšení výsledků.

Thèse de doctorat présentée en vue de l'obtention du doctorat en psychologie - recherche intervention, option neuropsychologie clinique (Ph.D) / Les oscillations endogènes cérébrales sont associées à des fonctions cognitives spécifiques et jouent un rôle important dans la communication entre les différentes régions corticales et sous-corticales. Les rythmes alpha (8-12 Hz) et bêta (13-30 Hz) ont été observés de façon dominante dans les aires sensorimotrices, avec des moyennes de fréquence autour de 10 et 20 Hz, et jouent un rôle dans les fonctions motrices. Ces oscillations cérébrales peuvent être entrainées par une stimulation externe, notamment par la stimulation électrique transcrânienne par courant alternatif (SEtCA). Ainsi, la SEtCA de 10 et 20 Hz a un effet sur certaines mesures physiologiques comme l’excitabilité corticospinale et la puissance des oscillations via la stimulation magnétique transcrânienne (SMT) et l’électroencéphalogramme (EEG), respectivement. Toutefois, les effets post-stimulation sont variables et parfois incohérents. De plus, à ce jour, aucune étude n’a mesuré les effets physiologiques d’une stimulation bilatérale sensorimotrice tant sur l’activité locale que sur l’interaction entre les deux aires sensorimotrices. Les articles composant le présent ouvrage visent à explorer les effets post-stimulation de deux fréquences de stimulation, soit 10 Hz et 20 Hz, sur les régions sensorimotrices à l’aide d’un montage SEtCA bilatéral. Ce travail de recherche s’est effectué à travers une revue de la littérature ainsi que deux études avec des paramètres méthodologiques relativement similaires, mais avec des mesures différentes et complémentaires de SMT et d’EEG. L’article 1 sert d’assise à la pertinence de l’évaluation de la connectivité entre le cortex moteur et les différentes aires du cerveau. Cet excursus recense et décrit les différents protocoles de stimulation magnétique pairée qui ont été développés au cours des dernières années afin d’évaluer la connectivité effective entre les aires sensorimotrices du cerveau. L’article 2 montre que la SEtCA bilatérale à 10 Hz a permis de réduire l’excitabilité corticospinale via la SMT après la stimulation. La fréquence bêta de 20 Hz n’a cependant mené à aucun changement. De plus, la SEtCA n’a pas modulé de façon significative les mesures d’interaction entre les régions sensorimotrices, telles l’inhibition interhémisphérique et les mouvements miroirs physiologiques. Dans l’article 3, les résultats démontrent que la SEtCA bilatérale à 10 et 20 Hz appliquée sur les aires sensorimotrices peut modifier la puissance des oscillations alpha et bêta après la stimulation. Notons que les résultats étaient associés à une variabilité interindividuelle qui est également rapportée dans la littérature. Ces résultats peuvent avoir des implications dans la conception de protocoles visant à induire des changements persistants dans l'activité cérébrale. / Endogenous brain oscillations are associated with specific cognitive functions and are known to have an important role in regimenting communication between cortical and subcortical areas. Alpha (8-12 Hz) and beta (13-30 Hz) rhythms have been observed predominantly in sensorimotor areas, with averages around 10 and 20 Hz, and are believed to play a role in motor functions. These cerebral oscillations can be entrained by external stimulation, in particular by transcranial alternating current stimulation (tACS). Thus, tACS has shown an impact on certain physiological measures such as corticospinal excitability and the power of oscillations via transcranial magnetic stimulation (TMS) and electroencephalogram (EEG), respectively. However, the after-effects are variable and incoherent. In addition, to date no study has measured the physiological effects of a bilateral sensorimotor stimulation montage on both local activity and the interaction between the two sensorimotor areas. Thus, the studies included in the present thesis aim to explore the after-effects of two stimulation frequencies, 10 Hz and 20 Hz, on sensorimotor regions using a bilateral montage. This research was carried out through a review of the literature as well as two methodological studies with relatively similar parameters, but using different and complementary measures of TMS and EEG. Article 1 provides a basis for the relevance of assessing the connectivity between the motor cortex and different areas of the brain. This excursus identifies and describes the different paired magnetic stimulation protocols that have been developed in recent years to assess the effective connectivity between sensorimotor areas of the brain. Study 2 shows that bilateral 10 Hz tACS significantly reduced corticospinal excitability via TMS after stimulation. However, the 20 Hz frequency did not lead to any change. In addition, tACS did not significantly modulate measures of interaction between sensorimotor regions, such as interhemispheric inhibition and physiological mirror movements. In study 3, the results failed to demonstrate reliably that bilateral tACS at 10 and 20 Hz administered over sensorimotor areas could modulate offline alpha and beta oscillations power at the stimulation site. Note that the results were associated with inter-individual variability, which is also reported in the literature. These findings may have implications for the design and implementation of future protocols aiming to induce sustained changes in brain activity.

<p> Although interest in brain-computer interfaces (BCIs) from researchers and consumers continues to increase, many BCIs lack the complexity and imaginative properties thought to guide users towards successful brain activity modulation. In this research, an experimental story brain-computer interface (ES-BCI) was developed, with which users could interact using cognitive strategies; specifically, thinking about the story and engaging with the main character of the story through their thought processes. In this system, the user’s frontal alpha asymmetry (FAA) measured with electroencephalography (EEG) was linearly mapped to the color saturation of the main character in the story. Therefore, the color saturation of the main character increased as FAA recorded from the participant’s brain activity increased above the FAA threshold required to receive visual feedback. A user-friendly experimental design was implemented using a comfortable EEG device and short neurofeedback (NF) training protocol. Eight distinct story scenes, each with a View and Engage NF component were created, and are referred to as blocks. In this system, seven out of 19 participants successfully increased FAA during the course of the study, for a total of ten successful blocks out of 152. Results concerning left (Lact) and right (Ract) prefrontal cortical activity contributions to FAA in both successful and unsuccessful blocks were examined to understand FAA measurements in greater detail. Additionally, electrodermal activity data (EDA) and self-reported questionnaire data were investigated to understand the user experience with this ES-BCI. Results suggest the potential of ES-BCI environments for engaging users and allowing for FAA modulation. New research directions for artistic BCIs investigating affect are discussed. </p>