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In der vorliegenden Untersuchung wurde ein arbeitspsychologisches Problem thematisiert, dass in Mensch-Maschine-Systemen auftritt. In Mensch-Maschine-Systemen werden Informationen in kodierter Form ausgetauscht. Diese inhaltlich verkürzte Informationsübertragung hat den Vorteil, keine lange Zustandsbeschreibung zu benötigen, so dass der Mensch auf die veränderten Zustände schnell und effizient reagieren kann. Dies wird aber nur dann ermöglicht, wenn der Mensch die kodierten Informationen (Kodes) vorher erlernten Bedeutungen zuordnen kann. Je nach Art der kodierten Informationen (visuelle, akustische oder alphanumerische Signale) wurden Gestaltungsempfehlungen für Kodealphabete entwickelt. Für Operateure resultiert die mentale Belastung durch Dekodierungsprozesse vor allem aus dem Umfang des Kodealphabetes (Anzahl von Kodezeichen), der wahrnehmungsmäßigen Gestaltung der Kodes und den Regeln über die Zuordnung von Bedeutungen zu Kodezeichen. Die Entscheidung über die Güte von Kodealphabeten geschieht in der Arbeitspsychologie in der Regel über Leistungsindikatoren. Dies sind üblicherweise die zur Dekodierung der Kodes benötigte Zeit und dabei auftretende Zuordnungsfehler. Psychophysiologische Daten werden oft nicht herangezogen. Fraglich ist allerdings, ob Zeiten und Fehler allein verlässliche Indikatoren für den kognitiven Aufwand bei Dekodierungsprozessen sind, da im hochgeübten Zustand bei gleichen Alphabetlängen, aber unterschiedlicher Kodezeichengestaltung sich häufig die mittleren Dekodierungszeiten zwischen Kodealphabeten nicht signifikant unterscheiden und Fehler überhaupt nicht auftreten. Die in der vorliegenden Arbeit postulierte Notwendigkeit der Ableitung von Biosignalen gründet sich auf die Annahme, dass mit ihrer Hilfe zusätzliche Informationen über die mentale Beanspruchung bei Dekodierungsprozessen gewonnen werden können, die mit der Erhebung von Leistungsdaten nicht erfasst werden. Denn gerade dann, wenn sich die Leistungsdaten zweier Kodealphabete nicht unterscheiden, können psychophysiologische Daten unterschiedliche Aspekte mentaler Beanspruchung erfassen, die mit Hilfe von Leistungsdaten nicht bestimmt werden können. Daher wird in Erweiterung des etablierten Untersuchungsansatzes vorgeschlagen, Biosignale als dritten Datenbereich, neben Leistungsdaten und subjektiven Daten mentaler Beanspruchung, abzuleiten, um zusätzliche Informationen über die mentale Beanspruchung bei Dekodierungsprozessen zu erhalten. Diese Annahme sollte mit Hilfe der Ableitung von Biosignalen überprüft werden. Der Begriff mentaler Beanspruchung wird in der bisherigen Literatur nur unzureichend definiert und differenziert. Daher wird zur Untersuchung dieses Konzepts, die wissenschaftliche Literatur berücksichtigend, ein erweitertes Modell mentaler Beanspruchung vorgestellt. Dabei wird die mentale Beanspruchung abgegrenzt von der emotionalen Beanspruchung. Mentale Beanspruchung wird weiterhin unterschieden in psychomotorische, perzeptive und kognitive Beanspruchung. Diese Aspekte mentaler Beanspruchung werden jeweils vom psychomotorischen, perzeptiven oder kognitiven Aufwand der zu bearbeitenden Aufgabe ausgelöst. In der vorliegenden Untersuchung wurden zwei zentrale Fragestellungen untersucht: Einerseits wurde die Analyse der anwendungsbezogenen Frage fokussiert, inwieweit psychophysiologische Indikatoren mentaler Beanspruchung über die Leistungsdaten (Dekodierungszeiten und Fehleranzahl) hinaus, zusätzliche Informationen zur Bestimmung der Güte von Kodealphabeten liefern. Andererseits wurde der Forschungsaspekt untersucht, inwieweit psychophysiologische Indikatoren mentaler Beanspruchung die zur Dekodierung notwendigen perzeptiven und kognitiven Aspekte mentaler Beanspruchung differenzieren können. Emotionale Beanspruchung war nicht Gegenstand der Analysen, weshalb in der Operationalisierung versucht wurde, sie weitgehend zu vermeiden. Psychomotorische Beanspruchung als dritter Aspekt mentaler Beanspruchung (neben perzeptiver und kognitiver Beanspruchung) wurde für beide Experimentalgruppen weitgehend konstant gehalten. In Lernexperimenten hatten zwei anhand eines Lern- und Gedächtnistests homogenisierte Stichproben jeweils die Bedeutung von 54 Kodes eines Kodealphabets zu erwerben. Dabei wurde jeder der zwei unahbhängigen Stichproben ein anderes Kodealphabet vorgelegt, wobei sich die Kodealphabete hinsichtlich Buchstabenanzahl (Kodelänge) und anzuwendender Zuordnungsregeln unterschieden. Damit differierten die Kodealphabete im perzeptiven und kognitiven Aspekt mentaler Beanspruchung. Die Kombination der Abkürzungen entsprach den in einer Feuerwehrleitzentrale verwendeten (Kurzbeschreibungen von Notfallsituationen). In der Lernphase wurden den Probanden zunächst die Kodealphabete geblockt mit ihren Bedeutungen präsentiert. Anschließend wurden die Kodes (ohne deren Bedeutung) in sechs aufeinanderfolgenden Prüfphasen randomisiert einzeln dargeboten, wobei die Probanden instruiert waren, die Bedeutung der jeweiligen Kodes in ein Mikrofon zu sprechen. Während des gesamten Experiments wurden, neben Leistungsdaten (Dekodierungszeiten und Fehleranzahl) und subjektiven Daten über die mentale Beanspruchung im Verlauf der Experimente, folgende zentralnervöse und peripherphysiologische Biosignale abgeleitet: Blutdruck, Herzrate, phasische und tonische elektrodermale Aktivität und Elektroenzephalogramm. Aus ihnen wurden zunächst 13 peripherphysiologische und 7 zentralnervöse Parameter berechnet, von denen 7 peripherphysiologische und 3 zentralnervöse Parameter die statistischen Voraussetzungen (Einschlusskriterien) soweit erfüllten, dass sie in die inferenzstatistische Datenanalyse einbezogen wurden. Leistungsdaten und subjektive Beanspruchungseinschätzungen der Versuchsdurchgänge wurden zu den psychophysiologischen Parametern in Beziehung gesetzt. Die Befunde zeigen, dass mittels der psychophysiologischen Daten zusätzliche Erkenntnisse über den kognitiven Aufwand gewonnen werden können. Als weitere Analyse wurden die Kodes post hoc in zwei neue Kodealphabete eingeteilt. Ziel dieser Analyse war es, die Unterschiede zwischen beiden Kodealphabeten zu erhöhen, um deutlichere reizbezogene psychophysiologische Unterschiede in den EEG-Daten zwischen den Kodealphabeten zu erhalten. Dazu wurde diejenigen, hinsichtlich ihrer Bedeutung, parallelen Kodes in beiden Kodealphabeten ausgewählt, die sich in der Dekodierungszeit maximal voneinander unterschieden. Eine erneute Analyse der EEG-Daten erbrachte jedoch keine Verbesserung der Ergebnisse. Drei Hauptergebnisse bezüglich der psychophysiologischen Parameter konnten festgestellt werden: Das erste Ergebnis ist für die psychophysiologische Methodik bedeutsam. Viele psychophysiologische Parameter unterschieden zwischen den Prüfphasen und zeigen damit eine hinreichende Sensitivität zur Untersuchung mentaler Beanspruchung bei Dekodierungsprozessen an. Dazu gehören die Anzahl der spontanen Hautleitwertsreaktionen, die Amplitude der Hautleitwertsreaktionen, das Hautleitwertsniveau, die Herzrate, die Herzratendifferenz und das Beta-2-Band des EEG. Diese Parameter zeigen einen ähnlichen Verlauf wie die Leistungsdaten. Dies zeigt, dass es möglich ist, die hier operationaliserte Art mentaler Beanspruchung in Form von Dekodierungsprozessen psychophysiologisch zu analysieren. Ein zweites Ergebnis betrifft die Möglichkeit, Unterschiede mentaler Beanspruchung zwischen beiden Gruppen psychophysiologisch abzubilden: Das Hautleitwertsniveau und das Theta-Frequenzband des Spontan-EEG zeigten Unterschiede zwischen beiden Stichproben von der ersten Prüfphase an. Diese Parameter indizieren unterschiedlichen kognitiven Aufwand in beiden Stichproben über alle Prüfphasen. Das wichtigste Ergebnis betrifft die Frage nach einem Informationsgewinn bei Einsatz psychophysiologischer Methoden zur Bewertung der Güte von Kodealphabeten: Einen tatsächlichen Informationsgewinn gegenüber den Leistungsdaten zeigte die Amplitude der elektrodermalen Aktivität und die Herzraten-Differenz an. Denn in den späteren Prüfphasen, wenn sich die Leistungsdaten beider Kodealphabete nicht mehr unterschieden, konnten unterschiedliche Ausprägungen dieser psychophysiologischen Parameter zwischen beiden Kodealphabeten verzeichnet werden. Damit konnten unterschiedliche Aspekte mentaler Beanspruchung in beiden Kodealphabeten in den späteren Prüfphasen erfasst werden, in denen sich die Leistungsdaten nicht mehr unterschieden. Alle drei Ergebnisse zeigen, dass es, trotz erheblichen technischen und methodischen Aufwands, sinnvoll erscheint, bei der Charakterisierung mentaler Belastungen und für die Gestaltung von Kodealphabeten auch psychophysiologische Daten heranzuziehen, da zusätzliche Informationen über den perzeptiven und kognitiven Dekodierungsaufwand gewonnen werden können.
The topic of synchronization forms a link between nonlinear dynamics and neuroscience. On the one hand, neurobiological research has shown that the synchronization of neuronal activity is an essential aspect of the working principle of the brain. On the other hand, recent advances in the physical theory have led to the discovery of the phenomenon of phase synchronization. A method of data analysis that is motivated by this finding - phase synchronization analysis - has already been successfully applied to empirical data. The present doctoral thesis ties up to these converging lines of research. Its subject are methodical contributions to the further development of phase synchronization analysis, as well as its application to event-related potentials, a form of EEG data that is especially important in the cognitive sciences. The methodical contributions of this work consist firstly in a number of specialized statistical tests for a difference in the synchronization strength in two different states of a system of two oscillators. Secondly, in regard of the many-channel character of EEG data an approach to multivariate phase synchronization analysis is presented. For the empirical investigation of neuronal synchronization a classic experiment on language processing was replicated, comparing the effect of a semantic violation in a sentence context with that of the manipulation of physical stimulus properties (font color). Here phase synchronization analysis detects a decrease of global synchronization for the semantic violation as well as an increase for the physical manipulation. In the latter case, by means of the multivariate analysis the global synchronization effect can be traced back to an interaction of symmetrically located brain areas.<BR> The findings presented show that the method of phase synchronization analysis motivated by physics is able to provide a relevant contribution to the investigation of event-related potentials in the cognitive sciences.
This thesis is concerned with the solution of the blind source separation problem (BSS). The BSS problem occurs frequently in various scientific and technical applications. In essence, it consists in separating meaningful underlying components out of a mixture of a multitude of superimposed signals. In the recent research literature there are two related approaches to the BSS problem: The first is known as Independent Component Analysis (ICA), where the goal is to transform the data such that the components become as independent as possible. The second is based on the notion of diagonality of certain characteristic matrices derived from the data. Here the goal is to transform the matrices such that they become as diagonal as possible. In this thesis we study the latter method of approximate joint diagonalization (AJD) to achieve a solution of the BSS problem. After an introduction to the general setting, the thesis provides an overview on particular choices for the set of target matrices that can be used for BSS by joint diagonalization. As the main contribution of the thesis, new algorithms for approximate joint diagonalization of several matrices with non-orthogonal transformations are developed. These newly developed algorithms will be tested on synthetic benchmark datasets and compared to other previous diagonalization algorithms. Applications of the BSS methods to biomedical signal processing are discussed and exemplified with real-life data sets of multi-channel biomagnetic recordings.
The goal of a Brain-Computer Interface (BCI) consists of the development of a unidirectional interface between a human and a computer to allow control of a device only via brain signals. While the BCI systems of almost all other groups require the user to be trained over several weeks or even months, the group of Prof. Dr. Klaus-Robert Müller in Berlin and Potsdam, which I belong to, was one of the first research groups in this field which used machine learning techniques on a large scale. The adaptivity of the processing system to the individual brain patterns of the subject confers huge advantages for the user. Thus BCI research is considered a hot topic in machine learning and computer science. It requires interdisciplinary cooperation between disparate fields such as neuroscience, since only by combining machine learning and signal processing techniques based on neurophysiological knowledge will the largest progress be made. In this work I particularly deal with my part of this project, which lies mainly in the area of computer science. I have considered the following three main points: <b>Establishing a performance measure based on information theory:</b> I have critically illuminated the assumptions of Shannon's information transfer rate for application in a BCI context. By establishing suitable coding strategies I was able to show that this theoretical measure approximates quite well to what is practically achieveable. <b>Transfer and development of suitable signal processing and machine learning techniques:</b> One substantial component of my work was to develop several machine learning and signal processing algorithms to improve the efficiency of a BCI. Based on the neurophysiological knowledge that several independent EEG features can be observed for some mental states, I have developed a method for combining different and maybe independent features which improved performance. In some cases the performance of the combination algorithm outperforms the best single performance by more than 50 %. Furthermore, I have theoretically and practically addressed via the development of suitable algorithms the question of the optimal number of classes which should be used for a BCI. It transpired that with BCI performances reported so far, three or four different mental states are optimal. For another extension I have combined ideas from signal processing with those of machine learning since a high gain can be achieved if the temporal filtering, i.e., the choice of frequency bands, is automatically adapted to each subject individually. <b>Implementation of the Berlin brain computer interface and realization of suitable experiments:</b> Finally a further substantial component of my work was to realize an online BCI system which includes the developed methods, but is also flexible enough to allow the simple realization of new algorithms and ideas. So far, bitrates of up to 40 bits per minute have been achieved with this system by absolutely untrained users which, compared to results of other groups, is highly successful.
In reading, word frequency is commonly regarded as the major bottom-up determinant for the speed of lexical access. Moreover, language processing depends on top-down information, such as the predictability of a word from a previous context. Yet, however, the exact role of top-down predictions in visual word recognition is poorly understood: They may rapidly affect lexical processes, or alternatively, influence only late post-lexical stages. To add evidence about the nature of top-down processes and their relation to bottom-up information in the timeline of word recognition, we examined influences of frequency and predictability on event-related potentials (ERPs) in several sentence reading studies. The results were related to eye movements from natural reading as well as to models of word recognition. As a first and major finding, interactions of frequency and predictability on ERP amplitudes consistently revealed top-down influences on lexical levels of word processing (Chapters 2 and 4). Second, frequency and predictability mediated relations between N400 amplitudes and fixation durations, pointing to their sensitivity to a common stage of word recognition; further, larger N400 amplitudes entailed longer fixation durations on the next word, a result providing evidence for ongoing processing beyond a fixation (Chapter 3). Third, influences of presentation rate on ERP frequency and predictability effects demonstrated that the time available for word processing critically co-determines the course of bottom-up and top-down influences (Chapter 4). Fourth, at a near-normal reading speed, an early predictability effect suggested the rapid comparison of top-down hypotheses with the actual visual input (Chapter 5). The present results are compatible with interactive models of word recognition assuming that early lexical processes depend on the concerted impact of bottom-up and top-down information. We offered a framework that reconciles the findings on a timeline of word recognition taking into account influences of frequency, predictability, and presentation rate (Chapter 4).
Intuitively, it is clear that neural processes and eye movements in reading are closely connected, but only few studies have investigated both signals simultaneously. Instead, the usual approach is to record them in separate experiments and to subsequently consolidate the results. However, studies using this approach have shown that it is feasible to coregister eye movements and EEG in natural reading and contributed greatly to the understanding of oculomotor processes in reading. The present thesis builds upon that work, assessing to what extent coregistration can be helpful for sentence processing research.
In the first study, we explore how well coregistration is suited to study subtle effects common to psycholinguistic experiments by investigating the effect of distance on dependency resolution. The results demonstrate that researchers must improve the signal-to-noise ratio to uncover more subdued effects in coregistration. In the second study, we compare oscillatory responses in different presentation modes. Using robust effects from world knowledge violations, we show that the generation and retrieval of memory traces may differ between natural reading and word-by-word presentation. In the third study, we bridge the gap between our knowledge of behavioral and neural responses to integration difficulties in reading by analyzing the EEG in the context of regressive saccades. We find the P600, a neural indicator of recovery processes, when readers make a regressive saccade in response to integration difficulties.
The results in the present thesis demonstrate that coregistration can be a useful tool for the study of sentence processing. However, they also show that it may not be suitable for some questions, especially if they involve subtle effects.
In this thesis sentence processing was investigated using a psychophysiological measure known as pupillometry as well as Event-Related Potentials (ERP). The scope of the the- sis was broad, investigating the processing of several different movement constructions with native speakers of English and second language learners of English, as well as word order and case marking in German speaking adults and children. Pupillometry and ERP allowed us to test competing linguistic theories and use novel methodologies to investigate the processing of word order. In doing so we also aimed to establish pupillometry as an effective way to investigate the processing of word order thus broadening the methodological spectrum.
The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.
Moving beyond ERP components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
Recent research suggests that the P3b may be closely related to the activation of the locus coeruleus-norepinephrine (LC-NE) system. To further study the potential association, we applied a novel technique, the non-invasive transcutaneous vagus nerve stimulation (tVNS), which is speculated to increase noradrenaline levels. Using a within-subject cross-over design, 20 healthy participants received continuous tVNS and sham stimulation on two consecutive days (stimulation counterbalanced across participants) while performing a visual oddball task. During stimulation, oval non-targets (standard), normal-head (easy) and rotated-head (difficult) targets, as well as novel stimuli (scenes) were presented. As an indirect marker of noradrenergic activation we also collected salivary alpha-amylase (sAA) before and after stimulation. Results showed larger P3b amplitudes for target, relative to standard stimuli, irrespective of stimulation condition. Exploratory post hoc analyses, however, revealed that, in comparison to standard stimuli, easy (but not difficult) targets produced larger P3b (but not P3a) amplitudes during active tVNS, compared to sham stimulation. For sAA levels, although main analyses did not show differential effects of stimulation, direct testing revealed that tVNS (but not sham stimulation) increased sAA levels after stimulation. Additionally, larger differences between tVNS and sham stimulation in P3b magnitudes for easy targets were associated with larger increase in sAA levels after tVNS, but not after sham stimulation. Despite preliminary evidence for a modulatory influence of tVNS on the P3b, which may be partly mediated by activation of the noradrenergic system, additional research in this field is clearly warranted. Future studies need to clarify whether tVNS also facilitates other processes, such as learning and memory, and whether tVNS can be used as therapeutic tool.