The Use of EEG Signals For Biometric Person Recognition

This work is devoted to investigating EEG-based biometric recognition systems. One potential advantage of using EEG signals for person recognition is the difficulty in generating artificial signals with biometric characteristics, thus making the spoofing of EEG-based biometric systems a challenging task. However, more works needs to be done to overcome certain drawbacks that currently prevent the adoption of EEG biometrics in real-life scenarios: 1) usually large number of employed sensors, 2) still relatively low recognition rates (compared with some other biometric modalities), 3) the template ageing effect. The existing shortcomings of EEG biometrics and their possible solutions are addressed from three main perspectives in the thesis: pre-processing, feature extraction and pattern classification. In pre-processing, task (stimuli) sensitivity and noise removal are investigated and discussed in separated chapters. For feature extraction, four novel features are proposed; for pattern classification, a new quality filtering method, and a novel instance-based learning algorithm are described in respective chapters. A self-collected database (Mobile Sensor Database) is employed to investigate some important biometric specified effects (e.g. the template ageing effect; using low-cost sensor for recognition). In the research for pre-processing, a training data accumulation scheme is developed, which improves the recognition performance by combining the data of different mental tasks for training; a new wavelet-based de-noising method is developed, its effectiveness in person identification is found to be considerable. Two novel features based on Empirical Mode Decomposition and Hilbert Transform are developed, which provided the best biometric performance amongst all the newly proposed features and other state-of-the-art features reported in the thesis; the other two newly developed wavelet-based features, while having slightly lower recognition accuracies, were computationally more efficient. The quality filtering algorithm is designed to employ the most informative EEG signal segments: experimental results indicate using a small subset of the available data for feature training could receive reasonable improvement in identification rate. The proposed instance-based template reconstruction learning algorithm has shown significant effectiveness when tested using both the publicly available and self-collected databases

Assessing Variability of EEG and ECG/HRV Time Series Signals Using a Variety of Non-Linear Methods

Time series signals, such as Electroencephalogram (EEG) and Electrocardiogram (ECG) represent the complex dynamic behaviours of biological systems. The analysis of these signals using variety of nonlinear methods is essential for understanding variability within EEG and ECG, which potentially could help unveiling hidden patterns related to underlying physiological mechanisms. EEG is a time varying signal, and electrodes for recording EEG at different positions on the scalp give different time varying signals. There might be correlation between these signals. It is important to know the correlation between EEG signals because it might tell whether or not brain activities from different areas are related. EEG and ECG might be related to each other because both of them are generated from one co-ordinately working body. Investigating this relationship is of interest because it may reveal information about the correlation between EEG and ECG signals. This thesis is about assessing variability of time series data, EEG and ECG, using variety of nonlinear measures. Although other research has looked into the correlation between EEGs using a limited number of electrodes and a limited number of combinations of electrode pairs, no research has investigated the correlation between EEG signals and distance between electrodes. Furthermore, no one has compared the correlation performance for participants with and without medical conditions. In my research, I have filled up these gaps by using a full range of electrodes and all possible combinations of electrode pairs analysed in Time Domain (TD). Cross-Correlation method is calculated on the processed EEG signals for different number unique electrode pairs from each datasets. In order to obtain the distance in centimetres (cm) between electrodes, a measuring tape was used. For most of our participants the head circumference range was 54-58cm, for which a medium-sized I have discovered that the correlation between EEG signals measured through electrodes is linearly dependent on the physical distance (straight-line) distance between them for datasets without medical condition, but not for datasets with medical conditions. Some research has investigated correlation between EEG and Heart Rate Variability (HRV) within limited brain areas and demonstrated the existence of correlation between EEG and HRV. But no research has indicated whether or not the correlation changes with brain area. Although Wavelet Transformations (WT) have been performed on time series data including EEG and HRV signals to extract certain features respectively by other research, so far correlation between WT signals of EEG and HRV has not been analysed. My research covers these gaps by conducting a thorough investigation of all electrodes on the human scalp in Frequency Domain (FD) as well as TD. For the reason of different sample rates of EEG and HRV, two different approaches (named as Method 1 and Method 2) are utilised to segment EEG signals and to calculate Pearson’s Correlation Coefficient for each of the EEG frequencies with each of the HRV frequencies in FD. I have demonstrated that EEG at the front area of the brain has a stronger correlation with HRV than that at the other area in a frequency domain. These findings are independent of both participants and brain hemispheres. Sample Entropy (SE) is used to predict complexity of time series data. Recent research has proposed new calculation methods for SE, aiming to improve the accuracy. To my knowledge, no one has attempted to reduce the computational time of SE calculation. I have developed a new calculation method for time series complexity which could improve computational time significantly in the context of calculating a correlation between EEG and HRV. The results have a parsimonious outcome of SE calculation by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain. Time series analysis method has been utilised to study complex systems that appear ubiquitous in nature, but limited to certain dynamic systems (e.g. analysing variables affecting stock values). In this thesis, I have also investigated the nature of the dynamic system of HRV. I have disclosed that Embedding Dimension could unveil two variables that determined HRV

EEG Biometrics During Sleep and Wakefulness: Performance Optimization and Security Implications

L’internet des objets et les mégadonnées ont un grand choix de domaines d’application. Dans les soins de santé ils ont le potentiel de déclencher les diagnostics à distance et le suivi en temps réel. Les capteurs pour la santé et la télémédecine promettent de fournir un moyen économique et efficace pour décentraliser des hôpitaux en soulageant leur charge. Dans ce type de système, la présence physique n’est pas contrôlée et peut engendrer des fraudes d’identité. Par conséquent, l'identité du patient doit être confirmée avant que n'importe quelle décision médicale ou financière soit prise basée sur les données surveillées. Des méthodes d’identification/authentification traditionnelles, telles que des mots de passe, peuvent être données à quelqu’un d’autre. Et la biométrie basée sur trait, telle que des empreintes digitales, peut ne pas couvrir le traitement entier et mènera à l’utilisation non autorisée post identification/authentification. Un corps naissant de recherche propose l’utilisation d’EEG puisqu’il présente des modèles uniques difficiles à émuler et utiles pour distinguer des sujets. Néanmoins, certains inconvénients doivent être surmontés pour rendre possible son adoption dans la vraie vie : 1) nombre d'électrodes, 2) identification/authentification continue pendant les différentes tâches cognitives et 3) la durée d’entraînement et de test. Pour adresser ces points faibles et leurs solutions possibles ; une perspective d'apprentissage machine a été employée. Premièrement, une base de données brute de 38 sujets aux étapes d'éveil (AWA) et de sommeil (Rem, S1, S2, SWS) a été employée. En effet, l'enregistrement se fait sur chaque sujet à l’aide de 19 électrodes EEG du cuir chevelu et ensuite des techniques de traitement de signal ont été appliquées pour enlever le bruit et faire l’extraction de 20 attribut dans le domaine fréquentiel. Deux ensembles de données supplémentaires ont été créés : SX (tous les stades de sommeil) et ALL (vigilance + tous les stades de sommeil), faisant 7 le nombre d’ensembles de données qui ont été analysés dans cette thèse. En outre, afin de tester les capacités d'identification et d'authentification tous ces ensembles de données ont été divises en les ensembles des Légitimes et des Intrus. Pour déterminer quels sujets devaient appartenir à l’ensemble des Légitimes, un ratio de validation croisée de 90-10% a été évalué avec différentes combinaisons en nombre de sujets. A la fin, un équilibre entre le nombre de sujets et la performance des algorithmes a été trouvé avec 21 sujets avec plus de 44 epochs dans chaque étape. Le reste (16 sujets) appartient à l’ensemble des Intrus.De plus, un ensemble Hold-out (4 epochs enlevées au hasard de chaque sujet dans l’ensemble des Légitimes) a été créé pour évaluer des résultats dans les données qui n'ont été jamais employées pendant l’entraînement.----------ABSTRACT : Internet of Things and Big Data have a variety of application domains. In healthcare they have the potential to give rise to remote health diagnostics and real-time monitoring. Health sensors and telemedicine applications promise to provide and economic and efficient way to ease patients load in hospitals. The lack of physical presence introduces security risks of identity fraud in this type of system. Therefore, patient's identity needs to be confirmed before any medical or financial decision is made based on the monitored data. Traditional identification/authentication methods, such as passwords, can be given to someone else. And trait-based biometrics, such as fingerprints, may not cover the entire treatment and will lead to unauthorized post-identification/authentication use. An emerging body of research proposes the use of EEG as it exhibits unique patterns difficult to emulate and useful to distinguish subjects. However certain drawbacks need to be overcome to make possible the adoption of EEG biometrics in real-life scenarios: 1) number of electrodes, 2) continuous identification/authentication during different brain stimulus and 3) enrollment and identification/authentication duration. To address these shortcomings and their possible solutions; a machine learning perspective has been applied. Firstly, a full night raw database of 38 subjects in wakefulness (AWA) and sleep stages (Rem, S1, S2, SWS) was used. The recording consists of 19 scalp EEG electrodes. Signal pre-processing techniques were applied to remove noise and extract 20 features in the frequency domain. Two additional datasets were created: SX (all sleep stages) and ALL (wakefulness + all sleep stages), making 7 the number of datasets that were analysed in this thesis. Furthermore, in order to test identification/authentication capabilities all these datasets were split in Legitimates and Intruders sets. To determine which subjects were going to belong to the Legitimates set, a 90-10% cross validation ratio was evaluated with different combinations in number of subjects. At the end, a balance between the number of subjects and algorithm performance was found with 21 subjects with over 44 epochs in each stage. The rest (16 subjects) belongs to the Intruders set. Also, a Hold out set (4 randomly removed epochs from each subject in the Legitimate set) was produced to evaluate results in data that has never been used during training

Time series causality analysis and EEG data analysis on music improvisation

This thesis describes a PhD project on time series causality analysis and applications. The project is motivated by two EEG measurements of music improvisation experiments, where we aim to use causality measures to construct neural networks to identify the neural differences between improvisation and non-improvisation. The research is based on mathematical backgrounds of time series analysis, information theory and network theory. We first studied a series of popular causality measures, namely, the Granger causality, partial directed coherence (PDC) and directed transfer function (DTF), transfer entropy (TE), conditional mutual information from mixed embedding (MIME) and partial MIME (PMIME), from which we proposed our new measures: the direct transfer entropy (DTE) and the wavelet-based extensions of MIME and PMIME. The new measures improved the properties and applications of their father measures, which were verified by simulations and examples. By comparing the measures we studied, MIME was found to be the most useful causality measure for our EEG analysis. Thus, we used MIME to construct both the intra-brain and cross-brain neural networks for musicians and listeners during the music performances. Neural differences were identified in terms of direction and distribution of neural information flows and activity of the large brain regions. Furthermore, we applied MIME on other EEG and financial data applications, where reasonable causality results were obtained.Open Acces

Comparison of data-driven analysis methods for identification of functional connectivity in fMRI

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 97-103).Data-driven analysis methods, such as independent component analysis (ICA) and clustering, have found a fruitful application in the analysis of functional magnetic resonance imaging (fMRI) data for identifying functionally connected brain networks. Unlike the traditional regression-based hypothesis-driven analysis methods, the principal advantage of data-driven methods is their applicability to experimental paradigms in the absence of a priori model of brain activity. Although ICA and clustering rely on very different assumptions on the underlying distributions, they produce surprisingly similar results for signals with large variation. The main goal of this thesis is to understand the factors that contribute to the differences in the identification of functional connectivity based on ICA and a more general version of clustering, Gaussian mixture model (GMM), and their relations. We provide a detailed empirical comparison of ICA and clustering based on GMM. We introduce a component-wise matching and comparison scheme of resulting ICA and GMM components based on their correlations. We apply this scheme to the synthetic fMRI data and investigate the influence of noise and length of time course on the performance of ICA and GMM, comparing with ground truth and with each other. For the real fMRI data, we propose a method of choosing a threshold to determine which of resulting components are meaningful to compare using the cumulative distribution function of their empirical correlations. In addition, we present an alternate method to model selection for selecting the optimal total number of components for ICA and GMM using the task-related and contrast functions. For extracting task-related components, we find that GMM outperforms ICA when the total number of components are less then ten and the performance between ICA and GMM is almost identical for larger numbers of the total components. Furthermore, we observe that about a third of the components of each model are meaningful to be compared to the components of the other.by Yongwook Bryce Kim.S.M

Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks

Dans cette thèse nous proposons un ensemble de contributions originales sous la forme de trois articles relatifs aux réseaux de neurones profonds réels et complexes. Nous abordons à la fois des problèmes théoriques et pratiques liés à leur apprentissage. Les trois articles traitent des méthodes conçues pour apporter des solutions aux problèmes de l’instabilité observée au cours de l’entrainement des réseaux, notamment le problème notoire de dilution et d’explosion des gradients ou «vanishing and exploding gradients » lors de l’entrainement des réseaux de neurones profonds. Nous proposons dans un premier temps la conception de modules d’entrainement appropriés, désignés par «building blocks», pour les réseaux de neurones profonds à valeurs complexes. Notre proposition comporte des méthodes d’initialisation et de normalisation ainsi que des fonctions d’activation des unités neuronales. Les modules conçus sont par la suite utilisés pour la spécification d’architectures profondes à valeurs complexes dédiées à accomplir diverses tâches. Ceci comprend des tâches de vision par ordinateur, de transcription musicale, de prédiction du spectre de la parole, d’extraction des signaux et de séparation des sources audio. Finalement nous procédons à une analyse détaillée de l’utilité de l’hypothèse contraignante d’orthogonalité généralement adoptée pour le paramétrage de la matrice de transition à travers les couches des réseaux de neurones réels récurrents.----------ABSTRACT : This thesis presents a set of original contributions in the form of three chapters on real and complex-valued deep neural networks. We address both theoretical issues and practical challenges related to the training of both real and complex-valued neural networks. First, we investigate the design of appropriate building blocks for deep complex-valued neural networks, such as initialization methods, normalization techniques and elementwise activation functions. We apply our theoretical insights to design building blocks for the construction of deep complex-valued architectures. We use them to perform various tasks in computer vision, music transcription, speech spectrum prediction, signal retrieval and audio source separation. We also perform an analysis of the usefulness of orthogonality for the hidden transition matrix in a real-valued recurrent neural network. Each of the three chapters are dedicated to dealing with methods designed to provide solutions to problems causing training instability, among them, the notorious problem of vanishing and exploding gradients during the training of deep neural networks. Throughout this manuscript we show the usefulness of the methods we propose in the context of well known challenges and clearly identifiable objectives. We provide below a summary of the contributions within each chapter. At present, the vast majority of building blocks, techniques, and architectures for training deep neural networks are based on real-valued computations and representations. However, representations based on complex numbers have started to receive increased attention. Despite their compelling properties complex-valued deep neural networks have been neglected due in part to the absence of the building blocks required to design and train this type of network. The lack of such a framework represents a noticeable gap in deep learning tooling

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods