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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.
Combined optimization of spatial and temporal filters for improving brain-computer interfacing
(2006)
Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output de ice by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.
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).
Brain-electric correlates of reading have traditionally been studied with word-by-word presentation, a condition that eliminates important aspects of the normal reading process and precludes direct comparisons between neural activity and oculomotor behavior. In the present study, we investigated effects of word predictability on eye movements (EM) and fixation-related brain potentials (FRPs) during natural sentence reading. Electroencephalogram (EEG) and EM (via video-based eye tracking) were recorded simultaneously while subjects read heterogeneous German sentences, moving their eyes freely over the text. FRPs were time-locked to first-pass reading fixations and analyzed according to the cloze probability of the currently fixated word. We replicated robust effects of word predictability on EMs and the N400 component in FRPs. The data were then used to model the relation among fixation duration, gaze duration, and N400 amplitude, and to trace the time course of EEG effects relative to effects in EM behavior. In an extended Methodological Discussion section, we review 4 technical and data-analytical problems that need to be addressed when FRPs are recorded in free-viewing situations (such as reading, visual search, or scene perception) and propose solutions. Results suggest that EEG recordings during normal vision are feasible and useful to consolidate findings from EEG and eye-tracking studies.
During natural reading, a parafoveal preview of the upcoming word facilitates its subsequent recognition (e.g., shorter fixation durations compared to masked preview) but nothing is known about the neural correlates of this so-called preview benefit. Furthermore, while the evidence is strong that readers preprocess orthographic features of upcoming words, it is controversial whether word meaning can also be accessed parafoveally. We investigated the timing, scope, and electrophysiological correlates of parafoveal information use in reading by simultaneously recording eye movements and fixation-related brain potentials (FRPs) while participants read word lists fluently from left to right. For one word the target (e.g., "blade") parafoveal information was manipulated by showing an identical ("blade"), semantically related ("knife"), or unrelated ("sugar") word as preview. In boundary trials, the preview was shown parafoveally but changed to the correct target word during the incoming saccade. Replicating classic findings, target words were fixated shorter after identical previews. In the EEG, this benefit was reflected in an occipitotemporal preview positivity between 200 and 280 ms. In contrast, there was no facilitation from related previews. In parafoveal-on-foveal trials, preview and target were embedded at neighboring list positions without a display change. Consecutive fixation of two related words produced N400 priming effects, but only shortly (160 ms) after the second word was directly fixated. Results demonstrate that neural responses to words are substantially altered by parafoveal preprocessing under normal reading conditions. We found no evidence that word meaning contributes to these effects. Saccade-contingent display manipulations can be combined with EEG recordings to study extrafoveal perception in vision.
Background: Empirical evidence suggests substantial deficits regarding emotion recognition in bulimia nervosa (BN). The aim of the current study was to investigate electrophysiologic evidence for deficits in emotional face processing in patients with BN. Methods: Event-related potentials were recorded from 13 women with BN and 13 matched healthy controls while viewing neutral, happy, fearful, and angry facial expressions. Participants' recognition performance for emotional faces was tested in a subsequent categorization task. In addition, the degree of alexithymia, depression, and anxiety were assessed via questionnaires. Results: Categorization of emotional faces was hampered in BN (p = .01). Amplitudes of event-related potentials differed during emotional face processing: face-specific N170 amplitudes were less pronounced for angry faces in patients with BN (mean [M] [standard deviation {SD}] = 1.46 [0.56] mu V versus M [SD] = -1.23 [0.61] mu V, p = .02). In contrast, P3 amplitudes were more pronounced in patients with BN as compared with controls (M [SD] = 2.64 [0.46] mu V versus M [SD] = 1.25 [0.39] mu V, p = .04), independent of emotional expression. Conclusions: The study provides novel electrophysiologic data showing that emotional faces are processed differently in patients with BN as compared with healthy controls. We suggest that deficits in early automatic emotion classification in BN are followed by an increased allocation of attentional resources to compensate for those deficits. These findings might contribute to a better understanding of the impaired social functioning in BN.
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.