The use of mobile phones is continually increasing throughout the world, with recent figures showing that there are currently more than 2 billion mobile phone users worldwide. However, despite the recognised benefits of the introduction and widespread use of mobile phone technologies, concerns regarding the potential health effects of exposure to the radiofrequency electromagnetic fields emitted by mobile phone handsets have similarly increased, leading to an increase in demand for scientific research to investigate the possibility of health effects related to the use of mobile phones. An increasing amount of radiofrequency bioeffects research related to mobile phone use has focussed on the possible effects of mobile phone exposure on human brain activity and function, particularly as the absorption of energy in the head and brain region is much higher than in other body regions, which is a direct result from the close proximity of the mobile phone to the head when in normal use. In particular, the use of sleep research has become a more widely used technique for assessing the possible effects of mobile phones on human health and wellbeing, and is particularly useful for providing important information in the establishment of possible radiofrequency bioeffects, especially in the investigation of potential changes in sleep architecture resulting from mobile phone use. A review of the previous literature showed that a number of studies have reported an increase in the electroencephalogram spectral power within the 8 � 14 Hz frequency range in both awake and sleep states following radiofrequency electromagnetic field exposure. In regards to sleep, the enhancements reported have not been entirely consistent, with some early studies failing to find an effect, while more recent studies have reported that the effect differs in terms of particular frequency range. However, in general the previous literature suggests that there is an effect of mobile phone emissions on the sleep electroencephalogram, particularly in the frequency range of sleep spindle activity. In addition to changes in spectral power, changes in other conventional sleep parameters and the production and secretion of melatonin have also been investigated, however, there has been little or no consistency in the findings of previous studies, with the majority of recent studies concluding that there is no influence of mobile phone radiofrequency fields on these parameters of sleep or melatonin. Following a detailed review of the previous research, the current study was developed with the aim to improve on previous methodological and statistical limitations, whilst also being the largest study to investigate mobile phone radiofrequency bioeffects on human sleep. The principle aims were thus to test for the immediate effects of mobile phone radiofrequency electromagnetic fields on human sleep architecture and the secretion of the pineal hormone, melatonin. The experiment included 50 participants who were randomly exposed to active and sham mobile phone exposure conditions (one week apart) for 30 minutes prior to a full night-time sleep episode. The experimental nights employed a randomised exposure schedule using a double-blind crossover design. Standard polysomnography was used to measure subsequent sleep, and in addition, participants were required to provide urine samples immediately following exposure and upon waking in the morning. A full dosimetric assessment of the exposure system was also performed in order to provide sufficient details of the exposure set-up used in the current thesis and to account for the lack of detailed dosimetric data provided in the majority of previous studies. The results of the current study suggest that acute exposure to a mobile phone prior to sleep significantly enhances electroencephalogram spectral power in the sleep spindle frequency range compared to the sham exposure condition. The current results also suggest that this mobile phone-induced enhancement in spectral power is largely transitory and does not linger throughout the night. Furthermore, a reduction in rapid eye movement sleep latency following mobile phone exposure was also found compared to the sham exposure, although interestingly, neither this change in rapid eye movement sleep latency or the enhancement in spectral power following mobile phone exposure, led to changes in the overall quality of sleep. Finally, the results regarding melatonin suggested that, overall, overnight melatonin secretion is unaffected by acute exposure to a mobile phone prior to sleep. In conclusion, the current study has confirmed that a short exposure to the radiofrequency electromagnetic fields emitted by a mobile phone handset immediately prior to sleep is sufficient to induce changes in brain activity in the initial part of sleep. The consequences or functional significance of this effect are currently unknown and it would be premature to draw conclusions about possible health consequences based on the findings of the current study.

The electroencephalogram (EEG) reflects summated, slow post-synaptic potentials of cortical neurons. Sensory, motor or cognitive events (such as a fast-rising sensory stimulus, a brisk self-paced movement or a stimulus-triggered cognitive task) can elicit transient changes in the ongoing human EEG, called event-related potentials (ERPs). ERPs are widely used in clinical practice, and believed to reflect the activity of the sensory system activated by the stimulus (for example, laser-evoked potentials are used to substantiate the neuropathic nature of clinical pain conditions). When ERPs are elicited by pairs or trains of stimuli delivered at short inter-stimulus intervals (ISIs), the magnitude of the ERP elicited by the repeated stimuli is markedly reduced, a phenomenon known as response decrement. While the interval between two consecutive stimuli becomes longer, the reduced response is recovered. Thus, this phenomenon has been traditionally interpreted in terms of neural refractoriness of generators of ERPs ("neural refractoriness hypothesis"). This thesis, however, challenges this neural refractoriness hypothesis by describing the results of manipulating the preceding events of the eliciting stimulus. The first study examined the effect of variable and short ISIs on sensory ERPs, delivering trains of auditory and electrical stimuli with random ISIs ranging from 100 to 1000ms. In the second study, pairs of laser stimuli were presented in two comparable conditions. In the constant condition, the ISI was identical across trials in each block, while in the variable condition, the ISI was variable across trials. By directly comparing ERPs elicited by laser stimulation, this study aimed to explore whether lack of saliency in the eliciting stimulus could explain the response decrement during stimulus repetition. Finally, the third study tested the hypothesis that the reduced eliciting ERPs would recover if saliency were introduced by changing the modality of the preceding event. Thus, trains of three stimuli (S1-S2-S3) with 1s ISI were presented; S2 was either same or different in modality as S1 and S3 in each block. Results from these three experiments demonstrate that this "refractoriness hypothesis" does not hold, and suggest that the magnitude of ERPs is only partly related to the magnitude of the incoming sensory input, and instead largely reflects neural activities triggered by salient events in the sensory environment. These results are important for the correct interpretation of ERPs in both physiological and clinical studies.

This research has developed a novel method which uses an easy to deploy single dry electrode wireless electroencephalogram (EEG) collection device as an input to an automated system that measures indicators of a participant’s attentiveness while they are watching a short training video. The results are promising, including 85% or better accuracy in identifying whether a participant is watching a segment of video from a boring scene or lecture, versus a segment of video from an attentiveness inducing active lesson or memory quiz. In addition, the final system produces an ensemble average of attentiveness across many participants, pinpointing areas in the training videos that induce peak attentiveness. Qualitative analysis of the results of this research is also very promising. The system produces attentiveness graphs for individual participants and these triangulate well with the thoughts and feelings those participants had during different parts of the videos, as described in their own words. As distance learning and computer based training become more popular, it is of great interest to measure if students are attentive to recorded lessons and short training videos. This research was motivated by this interest, as well as recent advances in electronic and computer engineering’s use of biometric signal analysis for the detection of affective (emotional) response. Signal processing of EEG has proven useful in measuring alertness, emotional state, and even towards very specific applications such as whether or not participants will recall television commercials days after they have seen them. This research extended these advances by creating an automated system which measures attentiveness towards short training videos. The bulk of the research was focused on electrical and computer engineering, specifically the optimization of signal processing algorithms for this particular application. A review of existing methods of EEG signal processing and feature extraction methods shows that there is a common subdivision of the steps that are used in different EEG applications. These steps include hardware sensing filtering and digitizing, noise removal, chopping the continuous EEG data into windows for processing, normalization, transformation to extract frequency or scale information, treatment of phase or shift information, and additional post-transformation noise reduction techniques. A large degree of variation exists in most of these steps within the currently documented state of the art. This research connected these varied methods into a single holistic model that allows for comparison and selection of optimal algorithms for this application. The research described herein provided for such a structured and orderly comparison of individual signal analysis and feature extraction methods. This study created a concise algorithmic approach in examining all the aforementioned steps. In doing so, the study provided the framework for a systematic approach which followed a rigorous participant cross validation so that options could be tested, compared and optimized. Novel signal analysis methods were also developed, using new techniques to choose parameters, which greatly improved performance. The research also utilizes machine learning to automatically categorize extracted features into measures of attentiveness. The research improved existing machine learning with novel methods, including a method of using per-participant baselines with kNN machine learning. This provided an optimal solution to extend current EEG signal analysis methods that were used in other applications, and refined them for use in the measurement of attentiveness towards short training videos. These algorithms are proven to be best via selection of optimal signal analysis and optimal machine learning steps identified through both n-fold and participant cross validation. The creation of this new system which uses signal processing of EEG for the detection of attentiveness towards short training videos has created a significant advance in the field of attentiveness measuring towards short training videos.

SUMMARY Introduction: There has recently been a great deal of interest in the role of synchronous high-frequency gamma oscillations in brain function. This interest has been motivated by an increasing body of evidence, that oscillations which are synchronous in phase across separated neuronal populations, may represent an important mechanism by which the brain binds or integrates spatially distributed processing activity which is related to the same object. Many models of schizophrenia suggest an impairment in the integration of brain processing, such as a loosening of associations, disconnection, defective multiple constraint organization, or cognitive dysmetria. This has led to recent speculation that abnormalities of high-frequency gamma synchronization may reflect a core dimension of the disturbance underlying this disorder. However, examination of the phase synchronization of gamma oscillations in patients with schizophrenia has never been previously undertaken. Method: In this thesis a new method of analysis of gamma synchrony was introduced, which enables the phase relationships of oscillations in a specific frequency band to be examined across multiple scalp sites as a function of time. This enabled, for the first time, the phase synchronization of gamma oscillations across widespread regions, to be studied in electrical brain activity measured at the scalp in humans. Gamma synchrony responses were studied in electroencephalographic (EEG) data acquired during a commonly employed conventional auditory oddball paradigm. The research consisted of two sets of experiments. In the first set of experiments, data from 100 normal subjects, consisting of 10 males and 10 females in each age decade from 20 to 70, was examined. These experiments were designed to characterize the gamma synchonizations that occurred in response to target and background stimuli and their functional significance in normal brain activity, and to exclude the possibility of these findings being due to electromyogram (EMG) or volume conduction artifact. The examination of functional significance involved the development of an additional new analysis technique. In the second set of experiments, data acquired from 35 patients with schizophrenia and 35 matched normal controls was analyzed. The purpose of these experiments was to determine whether patients showed disturbances of gamma synchrony compared to controls, and to establish the relationship of any such disturbances to medication levels, symptom profiles, duration of illness, and a range of psychophysiological variables. Results: In the 100 normals, responses to target stimuli were characterized by two bursts of synchronous gamma oscillations, an early (evoked) and a late (induced) synchronization, with different topographic distributions. Only the early gamma synchronization was seen in response to background stimuli. The main variable modulating the magnitude of these gamma synchronizations from epoch to epoch was pre-stimulus EEG theta (3-7 Hz) and delta (1-3 Hz) power. Early and late gamma synchrony were also associated with N1 and P3 ERP component amplitude across epochs. Across subjects, the early gamma synchronization was associated with shorter latency of the ERP components P2, N2 and P3, smaller amplitude of N1 and P2, and smaller pre-stimulus beta power. The control analyses showed that these gamma responses were specific to a narrow frequency range (37 to 41 Hz), and were not present in adjacent frequency bands. The responses were not generated by EMG contamination or volume conduction. In the 35 patients with schizophrenia, significant abnormalities of both the early and late synchronizations were observed compared to the 35 normal controls, with distinctive topographic characteristics. In general, early gamma synchrony was increased in patients compared to controls, and late gamma synchrony was decreased. These gamma synchrony disturbances were not related to medication level or the four summed symptom profile scores (positive, negative, general and total). They were, however, associated with duration of illness, becoming less severe the longer the patient had suffered from the disorder. The disordered gamma synchrony in patients was not secondary to abnormalities in other psychophysiological variables, but appeared to represent a primary disturbance. Discussion: The early synchronization may relate to the binding of object representations in early sensory processing, or, given that a constant inter-stimulus interval was employed, may be anticipatory and related to active memory. The late response is probably involved in binding in relation to activation of the internal contextual model involved in late expectancy/contextual processing (context updating or context closure) for target stimuli. The across epochs effects may relate to whether the focus of attention immediately prior to stimulus presentation is internal or is directed at the task. The across subjects effects suggest that a larger magnitude of the early gamma synchronization might indicate that the subject maintains a more stable and less ambiguous internal representation of the environment, that reduces the complexity of input and facilitates target/background discrimination and subsequent processing. The early gamma synchronization findings in patients with schizophrenia suggest that anticipatory processing involving active memory and forward-prediction of the environment is subject to over-binding or the formation of inappropriate associations. The late synchronization disturbances may reflect a fragmentation of contextual processing, and an inability to maintain contextual models of the environment intact over time. Conclusion: This research demonstrates the potential importance of integrative network activity as indexed by gamma phase synchrony in relation to normal cognition, and the possible broad relevance of such activity in psychiatric disorders. In particular, the application in this study to patients with schizophrenia showed that an impairment of brain integrative activity (missing links) might be a key feature of this illness.

Certain electrophysiological markers hold promise in distinguishing individuals with major depressive disorder (MDD) and in predicting antidepressant response, thereby assisting with assessment and optimizing treatment, respectively. This thesis examined resting brain activity via electroencephalographic (EEG) recordings, as well as EEG-derived event-related potentials (ERPs) to auditory stimuli and facial expression presentations in individuals with MDD and controls. Additionally, the utility of resting EEG as well as auditory ERPs (AEPs), and the associated loudness-dependence of AEPs (LDAEP) slope, were assessed in predicating outcome to chronic treatment with one of three antidepressant regimens [escitalopram (ESC); bupropion (BUP); ESC+BUP]. Relative to controls, depressed adults had lower pretreatment cortical activity in regions implicated in approach motives/positive processing. Increased anterior cingulate cortex (ACC)-localized theta was observed, possibly reflecting emotion/cognitive regulation disturbances in the disorder. AEPs and LDAEPs, putative indices of serotonin activity (implicated in MDD etiology), were largely unaltered in MDD. Assessment of ERPs to facial expression processing indicated slightly blunted late preconscious perceptual processing of expressions, and prolonged processing of intensely sad faces in MDD. Faces were rated as sadder overall in MDD, indicating a negative processing bias. Treatment responders (vs. non-responders) exhibited baseline cortical hypoactivity; after a week of treatment, cortical arousal emerged in responders. Increased baseline left fronto-cortical activity and early shifts towards this profile were noted in responders (vs. non-responders). Responders exhibited a steep, and non-responders shallow, baseline N1 LDAEP derived from primary auditory cortex activity. P2 LDAEP slopes (primary auditory cortex-derived) increased after a week of treatment in responders and decreased in non-responders. Consistent with overall findings, ESC responders displayed baseline cortical hypoactivity and steep LDAEP-sLORETA slopes (vs. non-responders). BUP responders also exhibited steep baseline slopes and high ACC theta. These results indicate that specific resting brain activity profiles appear to distinguish depressed from non-depressed individuals. Subtle ERP modulations to simple auditory and emotive processing also existed in MDD. Resting alpha power, ACC theta activity and LDAEP slopes predicted antidepressant response in general, but were limited in predicting outcome to a particular treatment, which may be associated with limited sample sizes.

Certain electrophysiological markers hold promise in distinguishing individuals with major depressive disorder (MDD) and in predicting antidepressant response, thereby assisting with assessment and optimizing treatment, respectively. This thesis examined resting brain activity via electroencephalographic (EEG) recordings, as well as EEG-derived event-related potentials (ERPs) to auditory stimuli and facial expression presentations in individuals with MDD and controls. Additionally, the utility of resting EEG as well as auditory ERPs (AEPs), and the associated loudness-dependence of AEPs (LDAEP) slope, were assessed in predicating outcome to chronic treatment with one of three antidepressant regimens [escitalopram (ESC); bupropion (BUP); ESC+BUP]. Relative to controls, depressed adults had lower pretreatment cortical activity in regions implicated in approach motives/positive processing. Increased anterior cingulate cortex (ACC)-localized theta was observed, possibly reflecting emotion/cognitive regulation disturbances in the disorder. AEPs and LDAEPs, putative indices of serotonin activity (implicated in MDD etiology), were largely unaltered in MDD. Assessment of ERPs to facial expression processing indicated slightly blunted late preconscious perceptual processing of expressions, and prolonged processing of intensely sad faces in MDD. Faces were rated as sadder overall in MDD, indicating a negative processing bias. Treatment responders (vs. non-responders) exhibited baseline cortical hypoactivity; after a week of treatment, cortical arousal emerged in responders. Increased baseline left fronto-cortical activity and early shifts towards this profile were noted in responders (vs. non-responders). Responders exhibited a steep, and non-responders shallow, baseline N1 LDAEP derived from primary auditory cortex activity. P2 LDAEP slopes (primary auditory cortex-derived) increased after a week of treatment in responders and decreased in non-responders. Consistent with overall findings, ESC responders displayed baseline cortical hypoactivity and steep LDAEP-sLORETA slopes (vs. non-responders). BUP responders also exhibited steep baseline slopes and high ACC theta. These results indicate that specific resting brain activity profiles appear to distinguish depressed from non-depressed individuals. Subtle ERP modulations to simple auditory and emotive processing also existed in MDD. Resting alpha power, ACC theta activity and LDAEP slopes predicted antidepressant response in general, but were limited in predicting outcome to a particular treatment, which may be associated with limited sample sizes.

SUMMARY Introduction: There has recently been a great deal of interest in the role of synchronous high-frequency gamma oscillations in brain function. This interest has been motivated by an increasing body of evidence, that oscillations which are synchronous in phase across separated neuronal populations, may represent an important mechanism by which the brain binds or integrates spatially distributed processing activity which is related to the same object. Many models of schizophrenia suggest an impairment in the integration of brain processing, such as a loosening of associations, disconnection, defective multiple constraint organization, or cognitive dysmetria. This has led to recent speculation that abnormalities of high-frequency gamma synchronization may reflect a core dimension of the disturbance underlying this disorder. However, examination of the phase synchronization of gamma oscillations in patients with schizophrenia has never been previously undertaken. Method: In this thesis a new method of analysis of gamma synchrony was introduced, which enables the phase relationships of oscillations in a specific frequency band to be examined across multiple scalp sites as a function of time. This enabled, for the first time, the phase synchronization of gamma oscillations across widespread regions, to be studied in electrical brain activity measured at the scalp in humans. Gamma synchrony responses were studied in electroencephalographic (EEG) data acquired during a commonly employed conventional auditory oddball paradigm. The research consisted of two sets of experiments. In the first set of experiments, data from 100 normal subjects, consisting of 10 males and 10 females in each age decade from 20 to 70, was examined. These experiments were designed to characterize the gamma synchonizations that occurred in response to target and background stimuli and their functional significance in normal brain activity, and to exclude the possibility of these findings being due to electromyogram (EMG) or volume conduction artifact. The examination of functional significance involved the development of an additional new analysis technique. In the second set of experiments, data acquired from 35 patients with schizophrenia and 35 matched normal controls was analyzed. The purpose of these experiments was to determine whether patients showed disturbances of gamma synchrony compared to controls, and to establish the relationship of any such disturbances to medication levels, symptom profiles, duration of illness, and a range of psychophysiological variables. Results: In the 100 normals, responses to target stimuli were characterized by two bursts of synchronous gamma oscillations, an early (evoked) and a late (induced) synchronization, with different topographic distributions. Only the early gamma synchronization was seen in response to background stimuli. The main variable modulating the magnitude of these gamma synchronizations from epoch to epoch was pre-stimulus EEG theta (3-7 Hz) and delta (1-3 Hz) power. Early and late gamma synchrony were also associated with N1 and P3 ERP component amplitude across epochs. Across subjects, the early gamma synchronization was associated with shorter latency of the ERP components P2, N2 and P3, smaller amplitude of N1 and P2, and smaller pre-stimulus beta power. The control analyses showed that these gamma responses were specific to a narrow frequency range (37 to 41 Hz), and were not present in adjacent frequency bands. The responses were not generated by EMG contamination or volume conduction. In the 35 patients with schizophrenia, significant abnormalities of both the early and late synchronizations were observed compared to the 35 normal controls, with distinctive topographic characteristics. In general, early gamma synchrony was increased in patients compared to controls, and late gamma synchrony was decreased. These gamma synchrony disturbances were not related to medication level or the four summed symptom profile scores (positive, negative, general and total). They were, however, associated with duration of illness, becoming less severe the longer the patient had suffered from the disorder. The disordered gamma synchrony in patients was not secondary to abnormalities in other psychophysiological variables, but appeared to represent a primary disturbance. Discussion: The early synchronization may relate to the binding of object representations in early sensory processing, or, given that a constant inter-stimulus interval was employed, may be anticipatory and related to active memory. The late response is probably involved in binding in relation to activation of the internal contextual model involved in late expectancy/contextual processing (context updating or context closure) for target stimuli. The across epochs effects may relate to whether the focus of attention immediately prior to stimulus presentation is internal or is directed at the task. The across subjects effects suggest that a larger magnitude of the early gamma synchronization might indicate that the subject maintains a more stable and less ambiguous internal representation of the environment, that reduces the complexity of input and facilitates target/background discrimination and subsequent processing. The early gamma synchronization findings in patients with schizophrenia suggest that anticipatory processing involving active memory and forward-prediction of the environment is subject to over-binding or the formation of inappropriate associations. The late synchronization disturbances may reflect a fragmentation of contextual processing, and an inability to maintain contextual models of the environment intact over time. Conclusion: This research demonstrates the potential importance of integrative network activity as indexed by gamma phase synchrony in relation to normal cognition, and the possible broad relevance of such activity in psychiatric disorders. In particular, the application in this study to patients with schizophrenia showed that an impairment of brain integrative activity (missing links) might be a key feature of this illness.

Alzheimer’s disease (AD) is a neurodegenerative disorder in which the death of brain cells causes memory loss and cognitive decline. As AD progresses, changes in the electrophysiological brain activity take place. Such changes can be recorded by the electroencephalography (EEG) and magnetoencephalography (MEG) techniques. These are the only two neurophysiologic approaches able to directly measure the activity of the brain cortex. Since EEGs and MEGs are considered as the outputs of a nonlinear system (i.e., brain), there has been an interest in nonlinear methods for the analysis of EEGs and MEGs. One of the most powerful nonlinear metrics used to assess the dynamical characteristics of signals is that of entropy. The aim of this thesis is to develop entropy-based approaches for characterization of EEGs and MEGs paying close attention to AD. Recent developments in the field of entropy for the characterization of physiological signals have tried: 1) to improve the stability and reliability of entropy-based results for short and long signals; and 2) to extend the univariate entropy methods to their multivariate cases to be able to reveal the patterns across channels. To enhance the stability of entropy-based values for short univariate signals, refined composite multiscale fuzzy entropy (MFE - RCMFE) is developed. To decrease the running time and increase the stability of the existing multivariate MFE (mvMFE) while keeping its benefits, the refined composite mvMFE (RCmvMFE) with a new fuzzy membership function is developed here as well. In spite of the interesting results obtained by these improvements, fuzzy entropy (FuzEn), RCMFE, and RCmvMFE may still lead to unreliable results for short signals and are not fast enough for real-time applications. To address these shortcomings, dispersion entropy (DispEn) and frequency-based DispEn (FDispEn), which are based on our introduced dispersion patterns and the Shannon’s definition of entropy, are developed. The computational cost of DispEn and FDispEn is O(N) – where N is the signal length –, compared with the O(N2) for popular sample entropy (SampEn) and FuzEn. DispEn and FDispEn also overcome the problem of equal values for embedded vectors and discarding some information with regard to the signal amplitudes encountered in permutation entropy (PerEn). Moreover, unlike PerEn, DispEn and FDispEn are relatively insensitive to noise. As extensions of our developed DispEn, multiscale DispEn (MDE) and multivariate MDE (mvMDE) are introduced to quantify the complexity of univariate and multivariate signals, respectively. MDE and mvMDE have the following advantages over the existing univariate and multivariate multiscale methods: 1) they are noticeably faster; 2) MDE and mvMDE result in smaller coefficient of variations for synthetic and real signals showing more stable profiles; 3) they better distinguish various states of biomedical signals; 4) MDE and mvMDE do not result in undefined values for short time series; and 5) mvMDE, compared with multivariate multiscale SampEn (mvMSE) and mvMFE, needs to store a considerably smaller number of elements. In this Thesis, two restating-state electrophysiological datasets related to AD are analyzed: 1) 148-channel MEGs recorded from 62 subjects (36 AD patients vs. 26 age-matched controls); and 2) 16-channel EEGs recorded from 22 subjects (11 AD patients vs. 11 age-matched controls). The results obtained by MDE and mvMDE suggest that the controls’ signals are more and less complex at respectively short (scales between 1 to 4) and longer (scales between 5 to 12) scale factors than AD patients’ recordings for both the EEG and MEG datasets. The p-values based on Mann-Whitney U-test for AD patients vs. controls show that the MDE and mvMDE, compared with the existing complexity techniques, significantly discriminate the controls from subjects with AD at a larger number of scale factors for both the EEG and MEG datasets. Moreover, the smallest p-values are achieved by MDE (e.g., 0.0010 and 0.0181 for respectively MDE and MFE using EEG dataset) and mvMDE (e.g., 0.0086 and 0.2372 for respectively mvMDE and mvMFE using EEG dataset) for both the EEG and MEG datasets, illustrating the superiority of these developed entropy-based techniques over the state-of-the-art univariate and multivariate entropy approaches. Overall, the introduced FDispEn, DispEn, MDE, and mvMDE methods are expected to be useful for the analysis of physiological signals due to their ability to distinguish different types of time series with a low computation time.

Lorsque l'on enregistre l'activité cérébrale en électroencéphalographie (EEG) de surface, le signal d'intérêt est fréquemment bruité par des activités différentes provenant de différentes sources de bruit telles que l'activité musculaire. Le débruitage de l'EEG est donc une étape de pré-traitement important dans certaines applications, telles que la localisation de source. Dans cette thèse, nous proposons six méthodes permettant la suppression du bruit de signaux EEG dans le cas particulier des activités enregistrées chez les patients épileptiques soit en période intercritique (pointes) soit en période critique (décharges). Les deux premières méthodes, qui sont fondées sur la décomposition généralisée en valeurs propres (GEVD) et sur le débruitage par séparation de sources (DSS), sont utilisées pour débruiter des signaux EEG épileptiques intercritiques. Pour extraire l'information a priori requise par GEVD et DSS, nous proposons une série d'étapes de prétraitement, comprenant la détection de pointes, l'extraction du support des pointes et le regroupement des pointes impliquées dans chaque source d'intérêt. Deux autres méthodes, appelées Temps Fréquence (TF) -GEVD et TF-DSS, sont également proposées afin de débruiter les signaux EEG critiques. Dans ce cas on extrait la signature temps-fréquence de la décharge critique par la méthode d'analyse de corrélation canonique. Nous proposons également une méthode d'Analyse en Composantes Indépendantes (ICA), appelé JDICA, basée sur une stratégie d'optimisation de type Jacobi. De plus, nous proposons un nouvel algorithme direct de décomposition canonique polyadique (CP), appelé SSD-CP, pour calculer la décomposition CP de tableaux à valeurs complexes. L'algorithme proposé est basé sur la décomposition de Schur simultanée (SSD) de matrices particulières dérivées du tableau à traiter. Nous proposons également un nouvel algorithme pour calculer la SSD de plusieurs matrices à valeurs complexes. Les deux derniers algorithmes sont utilisés pour débruiter des données intercritiques et critiques. Nous évaluons la performance des méthodes proposées pour débruiter les signaux EEG (simulés ou réels) présentant des activités intercritiques et critiques épileptiques bruitées par des artéfacts musculaires. Dans le cas des données simulées, l'efficacité de chacune de ces méthodes est évaluée d'une part en calculant l'erreur quadratique moyenne normalisée entre les signaux originaux et débruités, et d'autre part en comparant les résultats de localisation de sources, obtenus à partir des signaux non bruités, bruités, et débruités. Pour les données intercritiques et critiques, nous présentons également quelques exemples sur données réelles enregistrées chez des patients souffrant d'épilepsie partielle. / In the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing stages including spike peak detection, extraction of exact time support of spikes and clustering of spikes involved in each source of interest. Two other methods, called Time Frequency (TF)-GEVD and TF-DSS, are also proposed in order to denoise ictal EEG signals for which the time-frequency signature is extracted using the Canonical Correlation Analysis method. We also propose a deflationary Independent Component Analysis (ICA) method, called JDICA, that is based on Jacobi-like iterations. Moreover, we propose a new direct algorithm, called SSD-CP, to compute the Canonical Polyadic (CP) decomposition of complex-valued multi-way arrays. The proposed algorithm is based on the Simultaneous Schur Decomposition (SSD) of particular matrices derived from the array to process. We also propose a new Jacobi-like algorithm to calculate the SSD of several complex-valued matrices. The last two algorithms are used to denoise both interictal and ictal data. We evaluate the performance of the proposed methods to denoise both simulated and real epileptic EEG data with interictal or ictal activity contaminated with muscular activity. In the case of simulated data, the effectiveness of the proposed algorithms is evaluated in terms of Relative Root Mean Square Error between the original noise-free signals and the denoised ones, number of required ops and the location of the original and denoised epileptic sources. For both interictal and ictal data, we present some examples on real data recorded in patients with a drug-resistant partial epilepsy.

Certain electrophysiological markers hold promise in distinguishing individuals with major depressive disorder (MDD) and in predicting antidepressant response, thereby assisting with assessment and optimizing treatment, respectively. This thesis examined resting brain activity via electroencephalographic (EEG) recordings, as well as EEG-derived event-related potentials (ERPs) to auditory stimuli and facial expression presentations in individuals with MDD and controls. Additionally, the utility of resting EEG as well as auditory ERPs (AEPs), and the associated loudness-dependence of AEPs (LDAEP) slope, were assessed in predicating outcome to chronic treatment with one of three antidepressant regimens [escitalopram (ESC); bupropion (BUP); ESC+BUP]. Relative to controls, depressed adults had lower pretreatment cortical activity in regions implicated in approach motives/positive processing. Increased anterior cingulate cortex (ACC)-localized theta was observed, possibly reflecting emotion/cognitive regulation disturbances in the disorder. AEPs and LDAEPs, putative indices of serotonin activity (implicated in MDD etiology), were largely unaltered in MDD. Assessment of ERPs to facial expression processing indicated slightly blunted late preconscious perceptual processing of expressions, and prolonged processing of intensely sad faces in MDD. Faces were rated as sadder overall in MDD, indicating a negative processing bias. Treatment responders (vs. non-responders) exhibited baseline cortical hypoactivity; after a week of treatment, cortical arousal emerged in responders. Increased baseline left fronto-cortical activity and early shifts towards this profile were noted in responders (vs. non-responders). Responders exhibited a steep, and non-responders shallow, baseline N1 LDAEP derived from primary auditory cortex activity. P2 LDAEP slopes (primary auditory cortex-derived) increased after a week of treatment in responders and decreased in non-responders. Consistent with overall findings, ESC responders displayed baseline cortical hypoactivity and steep LDAEP-sLORETA slopes (vs. non-responders). BUP responders also exhibited steep baseline slopes and high ACC theta. These results indicate that specific resting brain activity profiles appear to distinguish depressed from non-depressed individuals. Subtle ERP modulations to simple auditory and emotive processing also existed in MDD. Resting alpha power, ACC theta activity and LDAEP slopes predicted antidepressant response in general, but were limited in predicting outcome to a particular treatment, which may be associated with limited sample sizes.