Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

Development of a System for the Training Assessment and Mental Workload Evaluation

Several studies have demonstrated that the main cause of accidents are due to Human Factor (HF) failures. Humans are the least and last controllable factor in the activity workflows, and the availability of tools able to provide objective information about the user’s cognitive state should be very helpful in maintain proper levels of safety. To overcome these issues, the objectives of the PhD covered three topics. The first phase was focused on the study of machine-learning techniques to evaluate the user’s mental workload during the execution of a task. In particular, the methodology was developed to address two important limitations: i) over-time reliability (no recalibration of the algorithm); ii) automatic brain features selection to avoid both the underfitting and overfitting problems. The second phase was dedicated to the study of the training assessment. In fact, the standard training evaluation methods do not provide any objective information about the amount of brain activation\resources required by the user, neither during the execution of the task, nor across the training sessions. Therefore, the aim of this phase was to define a neurophysiological methodology able to address such limitation. The third phase of the PhD consisted in overcoming the lack of neurophysiological studies regarding the evaluation of the cognitive control behaviour under which the user performs a task. The model introduced by Rasmussen was selected to seek neurometrics to characterize the skill, rule and knowledge behaviours by means of the user’s brain activity. The experiments were initially ran in controlled environments, whilst the final tests were carried out in realistic environments. The results demonstrated the validity of the developed algorithm and methodologies (2 patents pending) in solving the issues quoted initially. In addition, such results brought to the submission of a H2020-SMEINST project, for the realization of a device based on such results

Characterization of dynamical neural activity by means of EEG data: application to schizophrenia

Schizophrenia is a disabling, chronic and severe mental illness characterized by disintegration of the process of thinking, contact with reality and emotional responsiveness. Schizophrenia has been related to an aberrant assignment of salience to external objects and internal representations. In addition, schizophrenia has been identified as a dysconnection syndrome, which is associated with a reduced capacity to integrate information among different brain regions. Relevance attribution likely involves diverse cerebral regions and their interconnections. As a consequence, many efforts have been devoted to identifying abnormalities in the cortical connections and their relation to schizophrenia symptoms and cognitive performance. Neural oscillations are one of the largest contributing mechanism for enabling coordinated activity during normal brain functioning. Alterations in neural oscillations and cognitive processing in schizophrenia have long been assessed using electroencephalographic (EEG) recordings (i.e. time-varying voltages on the human scalp generated by the electrical activity on the cerebral cortex). Event-related potentials (ERP) depict EEG data as a response to a cognitive task. ERP analyses are used to gain further insights into the neural mechanisms underlying cognitive dysfunctions. In this Doctoral Thesis, a 3-stimulus auditory-oddball paradigm was used for examining cognitive processing as response to both relevant and irrelevant stimuli. A total of 69 ERP recordings were analyzed in the research papers included in the Thesis, which comprises 20 chronic schizophrenia patients, 11 first episode patients and 38 healthy controls. This Doctoral Thesis is focused on the study, design and application of biomedical signal processing methodologies in order to facilitate the understanding of cognitive processes altered by the schizophrenia. EEG data were examined using a two-level analysis: (I) local activation studies to quantify functional segregation of the brain network, by means of spectral analysis and by assessing neural source generators of P3a and P3b components; and (II) EEG interactions studies to explore functional integration across brain regions, including pair-wise couplings and exploring hierarchical organization of neural rhythms.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Automated Quantification of Human Alpha Rhythm

This thesis seeks to quantify human alpha rhythm in order to obtain better measures to test theoretical models of neurodynamics, with potential clinical applications for the method of identification. An automated algorithm is developed in Chapter 2 to facilitate collection of objective data from an expanded alpha band (4–14 Hz) in a large number of subjects. This method avoids subjective bias inherent to traditional visual identification of the alpha activity, produced multiple peak information (if multiple peaks exist) that is absent in qEEG measures, and uses information from multiple electrode sites to eliminate spurious peaks. This method is tested and validated on 100 subjects. In addition to traditional measures of alpha activities such as the frequency and amplitude, further measures were devised to better quantify the alpha rhythm and its spatial characteristics. Background spectra in the alpha range are also characterized. In Chapter 3 the algorithm is applied to a large (1498 subjects) database of normal healthy subjects of approximately equal number in each sex, as well as a large span in age (6–86 years), in order to establish typical parameter ranges. Analysis is done comparing the age and the topological trends that are known variants in the alpha rhythm. Investigations are also performed to test for potential sex differences and/or lateralities. Key results are that double alpha peaks are resolved in a large proportion of the subjects ( 50%), while single alpha peak cases are likely to be double-peak cases in which the two peaks are not resolved. Age trends in measures of alpha activity show increase of alpha frequency from childhood to adolescence, a plateau in adulthood, and a slight decline in old age; a decrease in alpha amplitude in old age is also observed. These findings are consistent with previous literature and provide better statistics. Topological distribution of the alpha activity on the head is also explored, with little lateral asymmetry observed. No statistically significant differences between the sexes are found. The C++ code that was developed and utilized in this thesis is included in Appendix B. It is provided on disk and is available online. A study carried out on the same group of subjects to determine age-related trends of EEG parameters produced by model fitting is included in Appendixes C, to provide a comparison between the methods and highlights corresponding results

Breaking Down the Barriers To Operator Workload Estimation: Advancing Algorithmic Handling of Temporal Non-Stationarity and Cross-Participant Differences for EEG Analysis Using Deep Learning

This research focuses on two barriers to using EEG data for workload assessment: day-to-day variability, and cross- participant applicability. Several signal processing techniques and deep learning approaches are evaluated in multi-task environments. These methods account for temporal, spatial, and frequential data dependencies. Variance of frequency- domain power distributions for cross-day workload classification is statistically significant. Skewness and kurtosis are not significant in an environment absent workload transitions, but are salient with transitions present. LSTMs improve day- to-day feature stationarity, decreasing error by 59% compared to previous best results. A multi-path convolutional recurrent model using bi-directional, residual recurrent layers significantly increases predictive accuracy and decreases cross-participant variance. Deep learning regression approaches are applied to a multi-task environment with workload transitions. Accounting for temporal dependence significantly reduces error and increases correlation compared to baselines. Visualization techniques for LSTM feature saliency are developed to understand EEG analysis model biases

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

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others