Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease

Wireless Channel Equalization in Digital Communication Systems

Our modern society has transformed to an information-demanding system, seeking voice, video, and data in quantities that could not be imagined even a decade ago. The mobility of communicators has added more challenges. One of the new challenges is to conceive highly reliable and fast communication system unaffected by the problems caused in the multipath fading wireless channels. Our quest is to remove one of the obstacles in the way of achieving ultimately fast and reliable wireless digital communication, namely Inter-Symbol Interference (ISI), the intensity of which makes the channel noise inconsequential. The theoretical background for wireless channels modeling and adaptive signal processing are covered in first two chapters of dissertation. The approach of this thesis is not based on one methodology but several algorithms and configurations that are proposed and examined to fight the ISI problem. There are two main categories of channel equalization techniques, supervised (training) and blind unsupervised (blind) modes. We have studied the application of a new and specially modified neural network requiring very short training period for the proper channel equalization in supervised mode. The promising performance in the graphs for this network is presented in chapter 4. For blind modes two distinctive methodologies are presented and studied. Chapter 3 covers the concept of multiple cooperative algorithms for the cases of two and three cooperative algorithms. The select absolutely larger equalized signal and majority vote methods have been used in 2-and 3-algoirithm systems respectively. Many of the demonstrated results are encouraging for further research. Chapter 5 involves the application of general concept of simulated annealing in blind mode equalization. A limited strategy of constant annealing noise is experimented for testing the simple algorithms used in multiple systems. Convergence to local stationary points of the cost function in parameter space is clearly demonstrated and that justifies the use of additional noise. The capability of the adding the random noise to release the algorithm from the local traps is established in several cases

Flexible methods for blind separation of complex signals

One of the main matter in Blind Source Separation (BSS) performed with a neural network approach is the choice of the nonlinear activation function (AF). In fact if the shape of the activation function is chosen as the cumulative density function (c.d.f.) of the original source the problem is solved. For this scope in this thesis a flexible approach is introduced and the shape of the activation functions is changed during the learning process using the so-called “spline functions”. The problem is complicated in the case of separation of complex sources where there is the problem of the dichotomy between analyticity and boundedness of the complex activation functions. The problem is solved introducing the “splitting function” model as activation function. The “splitting function” is a couple of “spline function” which wind off the real and the imaginary part of the complex activation function, each of one depending from the real and imaginary variable. A more realistic model is the “generalized splitting function”, which is formed by a couple of two bi-dimensional functions (surfaces), one for the real and one for the imaginary part of the complex function, each depending by both the real and imaginary part of the complex variable. Unfortunately the linear environment is unrealistic in many practical applications. In this way there is the need of extending BSS problem in the nonlinear environment: in this case both the activation function than the nonlinear distorting function are realized by the “splitting function” made of “spline function”. The complex and instantaneous separation in linear and nonlinear environment allow us to perform a complex-valued extension of the well-known INFOMAX algorithm in several practical situations, such as convolutive mixtures, fMRI signal analysis and bandpass signal transmission. In addition advanced characteristics on the proposed approach are introduced and deeply described. First of all it is shows as splines are universal nonlinear functions for BSS problem: they are able to perform separation in anyway. Then it is analyzed as the “splitting solution” allows the algorithm to obtain a phase recovery: usually there is a phase ambiguity. Finally a Cramér-Rao lower bound for ICA is discussed. Several experimental results, tested by different objective indexes, show the effectiveness of the proposed approaches

Flexible methods for blind separation of complex signals

One of the main matter in Blind Source Separation (BSS) performed with a neural network approach is the choice of the nonlinear activation function (AF). In fact if the shape of the activation function is chosen as the cumulative density function (c.d.f.) of the original source the problem is solved. For this scope in this thesis a flexible approach is introduced and the shape of the activation functions is changed during the learning process using the so-called “spline functions”. The problem is complicated in the case of separation of complex sources where there is the problem of the dichotomy between analyticity and boundedness of the complex activation functions. The problem is solved introducing the “splitting function” model as activation function. The “splitting function” is a couple of “spline function” which wind off the real and the imaginary part of the complex activation function, each of one depending from the real and imaginary variable. A more realistic model is the “generalized splitting function”, which is formed by a couple of two bi-dimensional functions (surfaces), one for the real and one for the imaginary part of the complex function, each depending by both the real and imaginary part of the complex variable. Unfortunately the linear environment is unrealistic in many practical applications. In this way there is the need of extending BSS problem in the nonlinear environment: in this case both the activation function than the nonlinear distorting function are realized by the “splitting function” made of “spline function”. The complex and instantaneous separation in linear and nonlinear environment allow us to perform a complex-valued extension of the well-known INFOMAX algorithm in several practical situations, such as convolutive mixtures, fMRI signal analysis and bandpass signal transmission. In addition advanced characteristics on the proposed approach are introduced and deeply described. First of all it is shows as splines are universal nonlinear functions for BSS problem: they are able to perform separation in anyway. Then it is analyzed as the “splitting solution” allows the algorithm to obtain a phase recovery: usually there is a phase ambiguity. Finally a Cramér-Rao lower bound for ICA is discussed. Several experimental results, tested by different objective indexes, show the effectiveness of the proposed approaches

Bibliometric Mapping of the Computational Intelligence Field

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation

Nichtlineare Merkmalsselektion mit der generalisierten Transinformation

In the context of information theory, the term Mutual Information has first been formulated by Claude Elwood Shannon. Information theory is the consistent mathematical description of technical communication systems. To this day, it is the basis of numerous applications in modern communications engineering and yet became indispensable in this field. This work is concerned with the development of a concept for nonlinear feature selection from scalar, multivariate data on the basis of the mutual information. From the viewpoint of modelling, the successful construction of a realistic model depends highly on the quality of the employed data. In the ideal case, high quality data simply consists of the relevant features for deriving the model. In this context, it is important to possess a suitable method for measuring the degree of the, mostly nonlinear, dependencies between input- and output variables. By means of such a measure, the relevant features could be specifically selected. During the course of this work, it will become evident that the mutual information is a valuable and feasible measure for this task and hence the method of choice for practical applications. Basically and without the claim of being exhaustive, there are two possible constellations that recommend the application of feature selection. On the one hand, feature selection plays an important role, if the computability of a derived system model cannot be guaranteed, due to a multitude of available features. On the other hand, the existence of very few data points with a significant number of features also recommends the employment of feature selection. The latter constellation is closely related to the so called "Curse of Dimensionality". The actual statement behind this is the necessity to reduce the dimensionality to obtain an adequate coverage of the data space. In other word, it is important to reduce the dimensionality of the data, since the coverage of the data space exponentially decreases, for a constant number of data points, with the dimensionality of the available data. In the context of mapping between input- and output space, this goal is ideally reached by selecting only the relevant features from the available data set. The basic idea for this work has its origin in the rather practical field of automotive engineering. It was motivated by the goals of a complex research project in which the nonlinear, dynamic dependencies among a multitude of sensor signals should be identified. The final goal of such activities was to derive so called virtual sensors from identified dependencies among the installed automotive sensors. This enables the real-time computability of the required variable without the expenses of additional hardware. The prospect of doing without additional computing hardware is a strong motive force in particular in automotive engineering. In this context, the major problem was to find a feasible method to capture the linear- as well as the nonlinear dependencies. As mentioned before, the goal of this work is the development of a flexibly applicable system for nonlinear feature selection. The important point here is to guarantee the practicable computability of the developed method even for high dimensional data spaces, which are rather realistic in technical environments. The employed measure for the feature selection process is based on the sophisticated concept of mutual information. The property of the mutual information, regarding its high sensitivity and specificity to linear- and nonlinear statistical dependencies, makes it the method of choice for the development of a highly flexible, nonlinear feature selection framework. In addition to the mere selection of relevant features, the developed framework is also applicable for the nonlinear analysis of the temporal influences of the selected features. Hence, a subsequent dynamic modelling can be performed more efficiently, since the proposed feature selection algorithm additionally provides information about the temporal dependencies between input- and output variables. In contrast to feature extraction techniques, the developed feature selection algorithm in this work has another considerable advantage. In the case of cost intensive measurements, the variables with the highest information content can be selected in a prior feasibility study. Hence, the developed method can also be employed to avoid redundance in the acquired data and thus prevent for additional costs.Der Begriff der Transinformation wurde erstmals von Claude Elwood Shannon im Kontext der Informationstheorie, einer einheitlichen mathematischen Beschreibung technischer Kommunikationssysteme, geprägt. Die vorliegenden Arbeit befaßt sich vor diesem Hintergrund mit der Entwicklung einer in der Praxis anwendbaren Methodik zur nichtlinearen Merkmalselektion quantitativer, multivariater Daten auf der Basis des bereits erwähnten informationstheoretischen Ansatzes der Transinformation. Der Erfolg beim Übergang von realen Meßdaten zu einer geeigneten Modellbeschreibung wird maßgeblich von der Qualität der verwendeten Datenmengen bestimmt. Eine qualitativ hochwertige Datenmenge besteht im Idealfall ausschließlich aus den für eine erfolgreiche Modellformulierung relevanten Daten. In diesem Kontext stellt sich daher sofort die Frage nach der Existenz eines geeigneten Maßes, um den Grad des, im Allgemeinen nichtlinearen, funktionalen Zusammenhangs zwischen Ein- und Ausgaben quantitativ korrekt erfassen zu können. Mit Hilfe einer solchen Größe können die relevanten Merkmale gezielt ausgewählt und somit von den redundanten Merkmalen getrennt werden. Im Verlaufe dieser Arbeit wird deutlich werden, daß die eingangs erwähnte Transinformation ein hierfür geeignetes Maß darstellt und im praktischen Einsatz bestens bestehen kann. Die ursprüngliche Motivation zur Erstellung der vorliegenden Arbeit hat ihren durchaus praktischen Hintergrund in der Automobiltechnik. Sie entstand im Rahmen eines komplexen Forschungsprojektes zur Ermittlung von nichtlinearen, dynamischen Zusammenhängen zwischen einer Vielzahl von meßtechnisch ermittelten Sensorsignalen. Das Ziel dieser Aktivitäten war, durch die Identifikation von nichtlinearen, dynamischen Zusammenhängen zwischen den im Automobil verbauten Sensoren, sog. virtuelle Sensoren abzuleiten. Die konkrete Aufgabenstellung bestand nun darin, die Bestimmung einer zentralen Motorgröße so effizient zu gestalten, daß diese ohne zusätzliche Hardware unter harten Echtzeitvorgaben berechenbar ist. Auf den zusätzlichen Einsatz von Hardware verzichten zu können und mit der bereits vorhandenen Rechenleistung auszukommen, stellt aufgrund des resultierenden, enormen Kostenaufwandes insbesondere in der Automobiltechnik eine unglaublich starke Motivation dar. In diesem Zusammenhang trat immer wieder die große Problematik zutage, eine praktisch berechenbare Methode zu finden, die sowohl lineare- als auch nichtlineare Zusammenhänge zuverlässig quantitativ erfassen kann. Im Verlauf der Arbeit werden nun unterschiedliche Selektionsstrategien mit der Transinformation kombiniert und deren Eigenschaften miteinander verglichen. In diesem Zusammenhang erweist sich die Kombination von Transinformation mit der sogenannten Forward Selection Strategie als besonders interessant. Es wird gezeigt, daß diese Kombination die praktische Berechenbarkeit für hochdimensionale Datenräume, im Vergleich zu anderen Vorgehensweisen, tatsächlich erst ermöglicht. Im Anschluß daran wird die Konvergenz dieses neuen Verfahrens zur Merkmalselektion bewiesen. Wir werden weiterhin sehen, daß die erzielten Ergebnisse bemerkenswert nahe an der optimalen Lösung liegen und im Vergleich mit einer alternativen Selektionsstrategie deutlich überlegen sind. Parallel zur eigentlichen Selektion der relevanten Merkmale ist es mit der in dieser Arbeit entwickelten Methode nun auch problemlos möglich, eine nichtlineare Analyse der zeitlichen Abhängigkeiten von ausgewählten Merkmalen durchzuführen. Eine anschließende dynamische Modellierung kann somit wesentlich effizienter durchgeführt werden, da die entwickelte Merkmalselektion zusätzliche Information hinsichtlich des dynamischen Zusammenhangs von Eingangs- und Ausgangsdaten liefert. Mit der in dieser Arbeit entwickelten Methode ist nun letztendlich gelungen was vorher nicht möglich war. Das quantitative Erfassen der nichtlinearen Zusammenhänge zwischen dedizierten Sensorsignalen, um diese in eine effiziente Merkmalselektion einfließen zu lassen. Im Gegensatz zur Merkmalsextraktion, hat die in diese Arbeit entwickelte Methode der nichtlinearen Merkmalselektion einen weiteren entscheidenden Vorteil. Insbesondere bei sehr kostenintensiven Messungen können diejenigen Variablen ausgewählt werden, die hinsichtlich der Abbildung auf eine Ausgangsgröße den höchsten Informationsgehalt tragen. Neben dem rein technischen Aspekt, die Selektionsentscheidung direkt auf den Informationsgehalt der verfügbaren Daten zu stützen, kann die entwickelte Methode ebenfalls im Vorfeld kostenrelevanter Entscheidungen herangezogen werden, um Redundanz und die damit verbundenen höheren Kosten gezielt zu vermeiden