An Electroencephalogram (EEG) Based Biometrics Investigation for Authentication: A Human-Computer Interaction (HCI) Approach

Encephalogram (EEG) devices are one of the active research areas in human-computer interaction (HCI). They provide a unique brain-machine interface (BMI) for interacting with a growing number of applications. EEG devices interface with computational systems, including traditional desktop computers and more recently mobile devices. These computational systems can be targeted by malicious users. There is clearly an opportunity to leverage EEG capabilities for increasing the efficiency of access control mechanisms, which are the first line of defense in any computational system. Access control mechanisms rely on a number of authenticators, including “what you know”, “what you have”, and “what you are”. The “what you are” authenticator, formally known as a biometrics authenticator, is increasingly gaining acceptance. It uses an individual’s unique features such as fingerprints and facial images to properly authenticate users. An emerging approach in physiological biometrics is cognitive biometrics, which measures brain’s response to stimuli. These stimuli can be measured by a number of devices, including EEG systems. This work shows an approach to authenticate users interacting with their computational devices through the use of EEG devices. The results demonstrate the feasibility of using a unique hard-to-forge trait as an absolute biometrics authenticator by exploiting the signals generated by different areas of the brain when exposed to visual stimuli. The outcome of this research highlights the importance of the prefrontal cortex and temporal lobes to capture unique responses to images that trigger emotional responses. Additionally, the utilization of logarithmic band power processing combined with LDA as the machine learning algorithm provides higher accuracy when compared against common spatial patterns or windowed means processing in combination with GMM and SVM machine learning algorithms. These results continue to validate the value of logarithmic band power processing and LDA when applied to oscillatory processes

Signal Processing and Machine Learning Techniques Towards Various Real-World Applications

abstract: Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Unsupervised Machine Learning for Networking:Techniques, Applications and Research Challenges

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications in various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances

Machine Learning in Image Analysis and Pattern Recognition

This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition

Unsupervised Machine Learning for Networking:Techniques, Applications and Research Challenges

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances

Video content analysis for automated detection and tracking of humans in CCTV surveillance applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented

Developmental neurocognitive pathway of psychosis proneness and the impact of cannabis use

Cette thèse fait la promotion d’une nouvelle approche ciblant le risque de psychose qui consiste à identifier les enfants et les jeunes adolescents de la communauté appartenant à différentes trajectoires développementales d’expériences psychotiques. Une identification très précoce du risque de psychose chez des jeunes de la communauté pourrait ainsi diminuer la période où les symptômes cliniques ne sont pas traités, mais aurait également le potentiel de prévenir efficacement l’émergence de symptômes avérés, et ce, si les facteurs de risque sont identifiés. Étant donné que la consommation de cannabis s’avère un important facteur de risque de la psychose et le contexte actuel où les états en sont à réviser leurs politiques de réglementation du cannabis, il s’avère primordial de mieux comprendre comment la consommation peut mener à la psychose chez les individus vulnérables. Tout d’abord, j’ai investigué la séquence temporelle entre la consommation de cannabis et les expériences psychotiques chez une population de 4000 adolescents, suivis pendant 4 ans, au moment de l’adolescence où les deux phénomènes s’initient. Ensuite, j’ai examiné, chez des adolescents suivant une trajectoire de vulnérabilité, le rôle d’un moins bon fonctionnement cognitif ainsi que celui d’une exacerbation des symptômes anxieux et dépressifs comme médiateurs du lien entre cannabis et risque de psychose. Enfin, j’ai investigué la présence de marqueurs neurocognitifs précoces (fonctionnels et structurels) qui seraient associés à l’émergence de symptômes psychotiques chez des adolescents, et exploré si la consommation de cannabis pourrait modérer l’ampleur de ces marqueurs. Les données proviennent de deux cohortes longitudinales suivant des adolescents de la population générale, l’étude Co-Venture (n=4000, âgés de 12 ans, suivis annuellement pendant 4 ans) et l’étude de neuroimagerie IMAGEN (n=2200, âgés de 14 ans, suivis pendant 2 ans), ainsi qu’un sous-échantillon de l’étude Co-Venture ayant complété des mesures de neuroimagerie (n=151, âgés de 12 ans, suivis annuellement pendant 4 ans). Les résultats ont montré que la consommation de cannabis précédait systématiquement l’augmentation des expériences psychotiques, et non l’inverse. Chez les jeunes suivant une trajectoire de vulnérabilité, la relation entre la consommation de cannabis et le risque de psychose était davantage expliquée par une augmentation des symptômes de dépression et d’anxiété. Une réduction du volume de l’hippocampe et de l’amygdale en combinaison avec une hyperactivité de ces mêmes régions en réponse à des expressions neutres étaient tous associés à l’émergence de symptômes psychotiques. Or, la consommation de cannabis n’a pas exacerbé les altérations structurelles observées chez les adolescents rapportant des expériences psychotiques. Ces résultats ont mis en évidence le rôle primordial d’un hyperfonctionnement du système limbique pouvant mener à l’attribution aberrante d’une importance émotionnelle aux stimuli de l’environnement, et ce, chez des adolescents vulnérables. Il semble que le mécanisme par lequel la consommation de cannabis mène à l’émergence de symptômes cliniques passe par son influence sur les symptômes de dépression et d’anxiété ainsi que leurs mécanismes neuronaux sous-jacents d’une hypersensibilité au stress. Enfin, de par ces résultats, cette thèse permet de contribuer au développement de nouvelles interventions visant à réduire la demande de cannabis chez des adolescents vulnérables.Following the worldwide initiative on intervening early in clinical high-risk individuals for psychosis, this thesis promotes a novel approach to identify those at risk for psychosis by studying children and adolescents from the community who report different trajectories of subclinical psychosis symptoms (i.e., psychotic-like experiences) without the confounds of iatrogenic effects such as major social and cognitive impairments. Early identification from this approach may not only reduce harm by shortening the duration of untreated symptoms, but may also have the capacity to prevent the emergence of clinically validated symptoms, particularly if early risk factors can be identified. Considering the long-standing notion that cannabis misuse is an important risk factor for psychosis and that jurisdictions around the world are currently revising their cannabis regulatory policies, there is a need to better understand how cannabis use may lead to psychosis in vulnerable youths. This thesis examined different mechanisms that may explain the complex relationship between cannabis use and psychosis risk. I first explored the temporal sequence between cannabis use and self-reported psychotic-like experiences in a population-based sample of 4000 adolescents, over a 4-year period when both phenomena have their onset. Second, in vulnerable youths, I investigated the role of impaired cognitive functioning as well as increased affective and anxious symptoms as mediators of the cannabis-to-psychosis relationship. And third, I explored the presence of early neurocognitive markers (both functional and structural) associated with the emergence of psychotic symptoms, and how cannabis use moderates these markers. Two longitudinal cohorts from the general population, the Co-Venture Study (n=4000, aged 12 years old, followed annually for 4 years) and the neuroimaging IMAGEN Study (n=2200, aged 14 years old, followed for 2 years), as well as the neuroimaging subsample from the Co-Venture Study (n=151, aged 12 years old, followed annually for 4 years) were used. It was found that an increase in cannabis use always preceded an increase in reported psychotic-like experiences throughout adolescence, but an increase in psychotic-like experiences rarely predicted an increase in cannabis use. Then, in vulnerable adolescents, the cannabis-to-psychosis risk relationship was better explained by increases in depression and anxiety symptoms relative to changes in cognitive functioning. It was demonstrated that reduced hippocampus and amygdala volumes, combined with hyperactivity of the same regions during neutral cues processing were associated with the emergence of psychotic symptoms in young adolescents reporting psychotic-like experiences. However, cannabis use did not exacerbate the structural alterations observed in youths with psychotic-like experiences. These findings have improved our understanding of the relationship between cannabis use and vulnerability to psychosis. They have also highlighted the important role of an impaired limbic network leading to an aberrant emotional salience attribution in vulnerable adolescents. Although cannabis use did not exacerbate brain structural alterations observed in vulnerable youths, it appears that cannabis will more likely interfere with depression and/or anxiety symptoms and their associated brain mechanisms underlying vulnerability to stress in the path towards psychosis risk. This thesis may inform the development of new evidence-based interventions that reduce demand for cannabis among vulnerable youths

Spatiotemporal anomaly detection: streaming architecture and algorithms

Includes bibliographical references.2020 Summer.Anomaly detection is the science of identifying one or more rare or unexplainable samples or events in a dataset or data stream. The field of anomaly detection has been extensively studied by mathematicians, statisticians, economists, engineers, and computer scientists. One open research question remains the design of distributed cloud-based architectures and algorithms that can accurately identify anomalies in previously unseen, unlabeled streaming, multivariate spatiotemporal data. With streaming data, time is of the essence, and insights are perishable. Real-world streaming spatiotemporal data originate from many sources, including mobile phones, supervisory control and data acquisition enabled (SCADA) devices, the internet-of-things (IoT), distributed sensor networks, and social media. Baseline experiments are performed on four (4) non-streaming, static anomaly detection multivariate datasets using unsupervised offline traditional machine learning (TML), and unsupervised neural network techniques. Multiple architectures, including autoencoders, generative adversarial networks, convolutional networks, and recurrent networks, are adapted for experimentation. Extensive experimentation demonstrates that neural networks produce superior detection accuracy over TML techniques. These same neural network architectures can be extended to process unlabeled spatiotemporal streaming using online learning. Space and time relationships are further exploited to provide additional insights and increased anomaly detection accuracy. A novel domain-independent architecture and set of algorithms called the Spatiotemporal Anomaly Detection Environment (STADE) is formulated. STADE is based on federated learning architecture. STADE streaming algorithms are based on a geographically unique, persistently executing neural networks using online stochastic gradient descent (SGD). STADE is designed to be pluggable, meaning that alternative algorithms may be substituted or combined to form an ensemble. STADE incorporates a Stream Anomaly Detector (SAD) and a Federated Anomaly Detector (FAD). The SAD executes at multiple locations on streaming data, while the FAD executes at a single server and identifies global patterns and relationships among the site anomalies. Each STADE site streams anomaly scores to the centralized FAD server for further spatiotemporal dependency analysis and logging. The FAD is based on recent advances in DNN-based federated learning. A STADE testbed is implemented to facilitate globally distributed experimentation using low-cost, commercial cloud infrastructure provided by Microsoft™. STADE testbed sites are situated in the cloud within each continent: Africa, Asia, Australia, Europe, North America, and South America. Communication occurs over the commercial internet. Three STADE case studies are investigated. The first case study processes commercial air traffic flows, the second case study processes global earthquake measurements, and the third case study processes social media (i.e., Twitter™) feeds. These case studies confirm that STADE is a viable architecture for the near real-time identification of anomalies in streaming data originating from (possibly) computationally disadvantaged, geographically dispersed sites. Moreover, the addition of the FAD provides enhanced anomaly detection capability. Since STADE is domain-independent, these findings can be easily extended to additional application domains and use cases

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes