Inhibitory Control (IC), a vital facet of childhood development, involves the ability to suppress a dominant response, as well as the ability to suppress irrelevant thoughts and behaviors. This ability emerges during the first year of life and develops rapidly during the preschool years. A variety of tasks have been developed to measure IC in this age group and, recently, research has demonstrated important differences in task performance according to various distinctions among these tasks. One under-researched distinction is that of whether an IC task requires the child to give a verbal or a motoric response. Therefore, the purpose of this study was to examine, in 4-year-old children, the differences and similarities among IC tasks requiring either a verbal or a motoric response. Differences were explored with respect to the contributions to verbal and motoric IC performance of language, intelligence, temperament, and frontal encephalography, as well as with respect to social and school readiness outcomes. IC was best described by a two-component model, distinguishing verbal and motoric IC. Both baseline and task electrophysiology contributed to task performance in the verbal Yes-No task as well as the motoric IC composite. Language and intelligence, too, were associated with both verbal and motoric IC, although nonverbal intelligence was less strongly correlated with verbal IC than it was with motoric IC. All laboratory measures of IC related to parent report of children’s IC as well as to other parent-reported temperament scales and factors. Children’s verbal and motoric IC were associated, too, with children’s social development, surprisingly showing the most consistent associations with social inhibition. Asocial behavior positively correlated more strongly with motoric IC than with verbal IC. Children’s laboratory IC positively correlated with their school readiness, even when controlling for their intelligence although children’s emergent literacy more positively related to their motoric, rather than verbal, IC. An interaction of intelligence and IC contributed to social variables, but not to school readiness. This research supports the important distinction between verbal and motoric IC, and demonstrates the utility of including an array of measures of both in early childhood research. / Ph. D.

Previous literature has suggested an apparent P300 sensitivity to self-relevant stimuli. To further explore this relationship, we asked participants to submit 10 photos, each of a particular category (e.g. footwear, plants), to be used as either targets or distractors in a given condition of an oddball task. Furthermore, we attempted to see whether the effect of self-relevance on the P300 could be induced in a participant by allowing them to study a set of unique photos which would then be used as targets. Our analysis suggested that P300 amplitude elicited in response to self-relevant stimuli used as targets was statistically significantly greater than all other conditions’ targets. This effect was not correlated with the participant sentiment toward their own photos as assessed by the Revised Personal Involvement Inventory. In light of this, we suggest a generalized effect of self-relevance on the P300. Limitations and future directions are discussed.

Neurological and developmental disorders (NDDs) impose an enormous burden of disease on children throughout the world. Two of the most common are autism spectrum disorder (ASD) and epilepsy. ASD has recently been estimated to affect 1 in 68 children, making it the most common neurodevelopmental disorder in children. Epilepsy is also a spectrum disorder that follows a developmental trajectory, with an estimated prevalence of 1%, nearly as common as autism. ASD and epilepsy co-occur in approximately 30% of individuals with a primary diagnosis of either disorder. Although considered to be different disorders, the relatively high comorbidity suggests the possibility of common neuropathological mechanisms. Early interventions for NDDs lead to better long-term outcomes. But early intervention is predicated on early detection. Behavioral measures have thus far proven ineffective in detecting autism before about 18 months of age, in part because the behavioral repertoire of infants is so limited. Similarly, no methods for detecting emerging epilepsy before seizures begin are currently known. Because atypical brain development is likely to precede overt behavioral manifestations by months or even years, a critical developmental window for early intervention may be opened by the discovery of brain based biomarkers. Analysis of brain activity with EEG may be under-utilized for clinical applications, especially for neurodevelopment. The hypothesis investigated in this dissertation is that new methods of nonlinear signal analysis, together with methods from biomedical informatics, can extract information from EEG data that enables detection of atypical neurodevelopment. This is tested using data collected at Boston Children’s Hospital. Several results are presented. First, infants with a family history of ASD were found to have EEG features that may enable autism to be detected as early as 9 months. Second, significant EEG-based differences were found between children with absence epilepsy, ASD and control groups using short 30-second EEG segments. Comparison of control groups using different EEG equipment supported the claim that EEG features could be computed that were independent of equipment and lab conditions. Finally, the potential for this technology to help meet the clinical need for neurodevelopmental screening and monitoring in low-income regions of the world is discussed.

Throughout the 1990s ADHD diagnosis and medication rates have increased rapidly, and this trend continues today. These sharp increases have been met with both public and clinical criticism, detractors stating over-diagnosis is a problem and healthy children are being unnecessarily medicated and labeled as disabled. However, others say that ADHD is being under-diagnosed in some populations. Critics often state that there are multiple factors that introduce subjectivity into the diagnosis process, meaning that a final diagnosis may be influenced by more than the desire to protect a patient's wellbeing. Some of these factors include standardized testing, legislation affecting special education funding, and the diagnostic process. In an effort to circumvent these extraneous factors, this work aims to further develop a potential method of using EEG signals to accurately discriminate between ADHD and non-ADHD children using features that capture spectral and perhaps temporal information from evoked EEG signals. KNN has been shown in prior research to be an effective tool in discriminating between ADHD and non-ADHD, therefore several different KNN models are created using features derived in a variety of fashions. One takes into account the heterogeneity of ADHD, and another one seeks to exploit differences in executive functioning of ADHD and non-ADHD subjects. The results of this classification method vary widely depending on the sample used to train and test the KNN model. With unfiltered Dataset 1 data over the entire ANT1 period, the most accurate EEG channel pair achieved an overall vector classification accuracy of 94%, and the 5th percentile of classification confidence was 80%. These metrics suggest that using KNN of EEG signals taken during the ANT task would be a useful diagnosis tool. However, the most accurate channel pair for unfiltered Dataset 2 data achieved an overall accuracy of 65% and a 5th percentile of classification confidence of 17%. The same method that worked so well for Dataset 1 did not work well for Dataset 2, and no conclusive reason for this difference was identified, although several methods to remove possible sources of noise were used. Using target time linked intervals did appear to marginally improve results in both Dataset 1 and Dataset 2. However, the changes in accuracy of intervals relative to target presentation vary between Dataset 1 and Dataset 2. Separating subjects into heterogeneity classes does appear to result in good (up to 83%) classification accuracy for some classes, but results are poor (about 50%) for other heterogeneity classes. A much larger data set is necessary to determine whether or not the very positive results found with Dataset 1 extend to a wide population. / Master of Science / Throughout the 1990s ADHD diagnosis and medication rates have increased rapidly, and this trend continues today. These sharp increases have been met with both public and clinical criticism, detractors stating over-diagnosis is a problem and healthy children are being unnecessarily medicated and labeled as disabled. However, others say that ADHD is being underdiagnosed in some populations. Critics often state that there are multiple factors that introduce subjectivity into the diagnosis process, meaning that a final diagnosis may be influenced by more than the desire to protect a patient’s wellbeing. Some of these factors include standardized testing, legislation affecting special education funding, and the diagnostic process. In an effort to circumvent these extraneous factors, this work aims to further develop a potential method of using EEG signals to accurately discriminate between ADHD and non-ADHD children using features that capture spectral and perhaps temporal information from evoked EEG signals. KNN has been shown in prior research to be an effective tool in discriminating between ADHD and non-ADHD, therefore several different machine learning models are created using features derived in a variety of fashions. One takes into account the heterogeneity of ADHD, and another one seeks to exploit differences in executive functioning of ADHD and non-ADHD subjects. The results of this classification method vary widely depending on the sample used to train and test the KNN model, classification accuracy has ranged from 65% to 94%, and the cause for this variation was not identified. A much larger data set is necessary to determine whether or not the very positive results found with Dataset 1 extend to a wide population.

As of 2016, diagnosis of ADHD in the US is controversial. Diagnosis of ADHD is based on subjective observations, and treatment is usually done through stimulants, which can have negative side-effects in the long term. Evidence shows that the probability of diagnosing a child with ADHD not only depends on the observations of parents, teachers, and behavioral scientists, but also on state-level special education policies. In light of these facts, unbiased, quantitative methods are needed for the diagnosis of ADHD. This problem has been tackled since the 1990s, and has resulted in methods that have not made it past the research stage and methods for which claimed performance could not be reproduced. This work proposes a combination of machine learning algorithms and signal processing techniques applied to EEG data in order to classify subjects with and without ADHD with high accuracy and confidence. More specifically, the K-nearest Neighbor algorithm and Gaussian-Mixture-Model-based Universal Background Models (GMM-UBM), along with autoregressive (AR) model features, are investigated and evaluated for the classification problem at hand. In this effort, classical KNN and GMM-UBM were also modified in order to account for uncertainty in diagnoses. Some of the major findings reported in this work include classification performance as high, if not higher, than those of the highest performing algorithms found in the literature. One of the major findings reported here is that activities that require attention help the discrimination of ADHD and Non-ADHD subjects. Mixing in EEG data from periods of rest or during eyes closed leads to loss of classification performance, to the point of approximating guessing when only resting EEG data is used. / Master of Science / As of 2016, diagnosis of ADHD in the US is controversial. Diagnosis of ADHD is based on subjective observations, and treatment is usually done through stimulants, which can have negative side-effects in the long term. Evidence shows that the probability of diagnosing a child with ADHD not only depends on the observations of parents, teachers, and behavioral scientists, but also on state-level special education policies. In light of these facts, unbiased, quantitative methods are needed for the diagnosis of ADHD. This problem has been tackled since the 1990s, and has resulted in methods that have not made it past the research stage and methods for which claimed performance could not be reproduced. This work proposes a combination of machine learning algorithms and signal processing techniques applied to EEG data in order to classify subjects with and without ADHD with high accuracy and confidence. Signal processing techniques are used to extract autoregressive (AR) coefficients, which contain information about brain activities and are used as “features”. Then, the features, extracted from datasets containing ADHD and Non-ADHD subjects, are used to create or train models that can classify subjects as either ADHD or Non-ADHD. Lastly, the models are tested using datasets that are different from the ones used in the previous stage, and performance is analyzed based on how many of the predicted labels (ADHD or Non-ADHD) match the expected labels. Some of the major findings reported in this work include classification performance as high, if not higher, than those of the highest performing algorithms found in the literature. One of the major findings reported here is that activities that require attention help the discrimination of ADHD and Non-ADHD subjects. Mixing in EEG data from periods of rest or during eyes closed leads to loss of classification performance, to the point of approximating guessing when only resting EEG data is used.

Seventy-three children participated in an investigation of inhibitory control (IC) at 3 years of age. Child IC was measured under various conditions in order to determine the impact that nonverbal and/or motivational task demands had on child IC task performance. Furthermore, task performance was examined with respect to measures of language, temperament, and psychophysiology. Tasks showed different patterns of relations to each of these variables. Furthermore, performance on the Hand Game, our measure of nonverbal IC, was explained by frontal EEG activity and, surprisingly, by language abilities. In contrast, performance on two other IC tasks, Day-Night and Less is More, was not related to measures of language or frontal EEG, perhaps because children performed at chance level on these tasks, indicating that these tasks may be too difficult for 3-year-old children. Implications of these findings are discussed. / Master of Science

No / The use of electroencephalograms (EEGs) to diagnose and analyses Alzheimer's disease (AD) has received much attention in recent years. The sample entropy (SE) has been widely applied to the diagnosis of AD. In our study, nine EEGs from 21 scalp electrodes in 3 AD patients and 9 EEGs from 3 age-matched controls are recorded. The calculations show that the kurtoses of the AD patients' EEG are positive and much higher than that of the controls. This finding encourages us to introduce a kurtosis-based de-noising method. The 21-electrode EEG is first decomposed using independent component analysis (ICA), and second sort them using their kurtoses in ascending order. Finally, the subspace of EEG signal using back projection of only the last five components is reconstructed. SE will be calculated after the above de-noising preprocess. The classifications show that this method can significantly improve the accuracy of SE-based diagnosis. The kurtosis analysis of EEG may contribute to increasing the understanding of brain dysfunction in AD in a statistical way.

The study’s objective was to assess whether resting-state regional functional connectivity and current source density (CSD) measured during smoking abstinence predict smoking progression across 18 months, depressive traits, and nicotine-enhanced reward sensitivity (NERS) in young light-nicotine (NIC) smokers using low-resolution brain electromagnetic tomography analysis (LORETA). A secondary goal was to assess whether depressive traits moderate the ability of connectivity and regional CSD to predict NERS. Brain regions of interest (ROIs) hypothesized to predict smoking progression, NERS, and depressive traits include structures with high-density nicotinic acetylcholine receptors (nAChRs) and reward-related areas. A total of N=108, 14-hour NIC-deprived young (age 18-24) light (5-35 NIC uses/week) smokers underwent electroencephalogram (EEG) recording while at rest (i.e., viewed a white crosshair on a black background) for 8 minutes then completed the PRT, an assessment of reward sensitivity, after smoking a placebo (0.05 mg NIC) and NIC (0.8 mg NIC) cigarette using a within-subjects design allowing for the assessment of NIC-induced changes in reward sensitivity. All EEG power and LORETA activity bands underwent regression analysis to discover if EEG-assessed brain activity can predict smoking progression, depressive traits, NERS, and their potential interaction. Localized brain regions include 1) reward-related structures, 2) depressive trait-related structures, and 3) large-scale neural (e.g., salience network (SN), default mode network (DMN), executive control network (ECN)) and substance use disorder networks (e.g., orbital frontal cortex (OFC), insula, dorsal lateral prefrontal cortex (dlPFC) anterior cingulate cortex (ACC)). Weaker resting-state connectivity (rsC) between the insula and ACC (i.e., SN) predicted greater smoking progression at 18 months (theta1 and theta2) and greater depressive traits (delta and theta1), while greater rsC within the SN predicted greater NERS (alpha2 and beta 2/3[23.19 – 25.14 Hz]). Greater NERS was also predicted by greater alpha2 connectivity between the 1) ACC and posterior cingulate cortex (PCC) and 2) ACC and left dlPFC. Greater depressive traits were also predicted by 1) weaker delta and theta2 connectivity between the bilateral insula, 2) weaker delta, theta1, and theta2 between the insula and dlPFC, 3) weaker delta and theta1 between the insula and subgenual cortex, 4) greater theta2 in the right vs. left default mode, and 5) greater delta (2.44 – 3.41 Hz) in the left vs. right default mode network. Both greater depressive traits and greater NERS were predicted by weaker 1) theta2/alpha1 (6.59 – 9.52 Hz) between the insula and dlPFC and 2) alpha1 (7.5 – 9.5 Hz) between the left orbital frontal cortex and right dlPFC. These findings provide the first evidence that differences in EEG-assessed brain connectivity in young light smokers are associated with nicotine-enhanced reward sensitivity, depressive traits, and smoking progression. Notably, weaker low-frequency rsC within the salience network predicted depressive traits and smoking progression, while greater high-frequency rsC predicted greater nicotine-enhanced reward sensitivity. These findings suggest that salience network rsC and drug-enhanced reward sensitivity may be useful tools and potential endophenotypes for reward sensitivity and drug-dependence research.

The analysis and understanding of physiological and brain signals is critical in order to decode user’s behavioral/neural outcome measures in different domain scenarios. Personal Health-Care agents have been proposed recently in order to monitor and acquire reliable data from daily activities to enhance control participants’ wellbeing, and the quality of life of multiple non-neurotypical participants in clinical lab-controlled studies. The inclusion of new wearable devices with increased and more compact memory requirements,and the possibility to include long-size datasets on the cloud and network-based applications agile the implementation of new improved computational health-care agents. These new enhanced agents are able to provide services including real time health-care,medical monitoring, and multiple biological outcome measures-based alarms for medicaldoctor diagnosis. In this dissertation we will focus on multiple Signal Processing (SP), Machine Learning (ML), Saliency Relevance Maps (SRM) techniques and classifiers with the purpose to enhance the Personal Health-care agents in a multimodal clinical environment. Therefore, we propose the evaluation of current state-of-the-art methods to evaluate the incidence of successful hypertension detection, categorical and emotion stimuli decoding using biosignals. To evaluate the performance of ML, SP, and SRM techniques proposed in this study, wedivide this thesis document in two main implementations: 1) Four different initial pipelines where we evaluate the SP, and ML methodologies included here for an enhanced a) Hypertension detection based on Blood-Volume-Pulse signal (BVP) and Photoplethysmography (PPG) wearable sensors, b) Heart-Rate (HR) and Inter-beat-interval (IBI) prediction using light adaptive filtering for physical exercise/real environments, c) Object Category stimuli decoding using EEG features and features subspace transformations, and d) Emotion recognition using EEG features from recognized datasets. And 2) A complete performance and robust SRM evaluation of a neural-based Emotion Decoding/Recognition pipeline using EEG features from Autism Spectrum Disorder (ASD) groups. This pipeline is presented as a novel assistive system for lab-controlled Face Emotion Recognition (FER) intervention ASD subjects. In this pipeline we include a Deep ConvNet asthe Deep classifier to extract the correct neural information and decode emotions successfully.

Proposé dans le cadre d’une triple collaboration entre le Centre de Recherche en Science et Technologie de l'Information et de la Communication (CReSTIC) de l'Université de Reims Champagne-Ardenne (URCA), le Laboratoire d'Ingénierie et Sciences des Matériaux (LISM) de l'Université de Reims Champagne-Ardenne (URCA) et le Laboratoire Electronique et Télécommunication (LET) de l'Université Mohammed 1er, ce projet a pour objectif d’associer les compétences de ces laboratoires, le traitement du signal pour le CReSTIC et le LET et la caractérisation de l’endommagement des agro matériaux composites pour le LISM. Le travail de cette thèse consiste à développer une méthode de Contrôle Non Destructif CND) par Emissions Acoustiques (EA). En effet, durant le processus de dégradation des matériaux composites (sollicitations mécaniques, vieillissement), plusieurs mécanismes d’endommagement à l’échelle microscopique peuvent intervenir selon la nature du composite et de ses constituants (fibres et résine). L’EA permet d’analyser et d’identifier plus en détails ces mécanismes d’endommagement, à l’aide d’une classification. Cette méthode de classification sera basée sur l’utilisation de méthodes à noyaux (typiquement séparateur à vastes marges) dans le domaine temps-fréquence. Il s’agira de déterminer un (ou des) noyau(x) adapté(s) au problème posé, basé sur une mesure de similarité entre les signaux. Ce travail permettra ainsi d’analyser et classifier les mécanismes d’endommagement sans emploi de descripteurs. Cette nouvelle méthode de CND par EA permettra de fournir des informations pertinentes et de vérifier efficacement la fiabilité et l'état de santé en temps réel de structures en service, sans perturber l'exploitation tout en réduisant les coûts de maintenance. / The aims of this thesis consists in developing a method of Non-destructive testing (NDT) by Acoustic Emissions (AE). Indeed, during the process of degradation of composite materials several mechanisms of damage in the microscopic scale can intervene according to the nature of the composite and its constituents (fibers and epoxy). AE allows to analyze and to identify more in detail these damages mechanism, by means of a classification. This method of classification will be based on the use of kernel methods in the time - frequency domain. It will be a question of determining one adapted kernel in the proposed problem. This work will so allow to analyze and to classify mechanisms without using descriptors.