Although the static measure of accommodation is well documented, the dynamic aspect of the resting state (dark focus) of accommodation is still unknown. Previous studies suggest that refractive error is minimal at the intermediate resting point of accommodation — i.e., at the dark focus distances. Additionally, aging is closely linked to increased refractive error. In order to assess the effects of age on dark focus distance and its utility in enhancing the visual information transfer rate, two experiments were conducted under nighttime condition (scotopic vision) in a laboratory setting. A total of forty participants with normal vision or corrected to normal vision were recruited from four different age groups (younger: 26.9±5.0 years; middle-aged: 50.7±4.8 years; young-old: 64.6±2.8 years; and old-old: 79.8±6.1 years). Each age group included ten participants. In Experiment I, the accommodative status of dark focus at the fovea was assessed objectively using the modified autorefractor, a newly developed method to continuously monitor the accommodation process. The mean dark focus distances for younger, middle-aged, young-old, and old-old adults were 64.5±6.6, 73.4±20.6, 84.4±29, and 92.1±33.4 cm, respectively. There was a significant difference between the dark focus distances among different age groups. Post-hoc analysis indicated that there were statistically significant differences among young and old-old, young and young-old, and middle-aged and old-old age groups. In Experiment II, the information transfer rate was determined while viewing a target at three different distances: 52 cm, 73 cm (current recommended reading distances) and the individual's dark focus. A set of randomized alphabet characters were presented on a visual display with a luminance level of 20 cd/m2 and ambient illumination level of 4 lux. To assess the information transfer rate, participants were asked to read a set of characters aloud with their fastest rate for three seconds. Three measurements of information transfer rate at each viewing distance at random were made. Results obtained from each viewing distance were collected and averaged. The results showed that the mean visual information transfer rate for younger, middle-aged, young-old, and old-old adults were 14.27±1.43, 10.58±2.25, 9.35±2.13, and 7.73±2.36 bits/sec, respectively. There were statistically significant differences at α < 0.05 in means and standard deviations of visual information transfer rate in young and old-old, young and young-old, young and middle-aged, and middle-aged and old-old age groups. The mean visual information transfer rate at 52 cm, 73 cm and individual dark focus were 11.08±3.10, 10.14±2.97, and 10.22±3.42 bits/sec, respectively. There were statistically significant differences at α < 0.05 in means and standard deviations of visual information transfer rate at different viewing distances at 52 cm and 73 cm, and 52 cm and individual's dark focus. However, there were no statistically significant differences in the interaction between age and viewing distance (F = 1.6818, P = 0.1378) on the amount of visual information transfer rate. In summary, the visual information transfer rate was not greater when presenting visual stimulus at the individual's dark focus as compared with two fixed recommended viewing distances (52 cm and 73 cm). The greatest amount of visual information gained was at 52 cm. Actual and potential applications of this study including specifications for designs were also discussed. / Ph. D.

No description available.

Brain-machine interfaces (BMIs) hold promise for restoring motor functions in severely paralyzed individuals. Invasive BMIs are capable of recording signals from individual neurons and typically provide the highest signal-to-noise ratio. Despite many efforts in the scientific community, BMI technology is still not reliable enough for widespread clinical application. The most prominent challenges include biocompatibility, stability, longevity, and lack of good models for informed signal processing and BMI comparison. To address the problem of low signal quality of chronic probes, in the first part of the thesis one such design, the Neurotrophic Electrode, was modified by increasing its channel capacity to form a Neurotrophic Array (NA). Specifically, single wires were replaced with stereotrodes and the total number of recording wires was increased. This new array design was tested in a rhesus macaque performing a delayed saccade task. The NA recorded little single unit spiking activity, and its local field potentials (LFPs) correlated with presented visual stimuli and saccade locations better than did extracted spikes. The second part of the thesis compares the NA to the Utah Array (UA), the only other micro-array approved for chronic implantation in a human brain. The UA recorded significantly more spiking units, which had larger amplitudes than NA spikes. This was likely due to differences in the array geometry and construction. LFPs on the NA electrodes were more correlated with each other than those on the UA. These correlations negatively impacted the NA's information capacity when considering more than one recording site. The final part of this dissertation applies information theory to develop objective measures of BMI performance. Currently, decoder information transfer rate (ITR) is the most popular BMI information performance metric. However, it is limited by the selected decoding algorithm and does not represent the full task information embedded in the recorded neural signal. A review of existing methods to estimate ITR is presented, and these methods are interpreted within a BMI context. A novel Gaussian mixture Monte Carlo method is developed to produce good ITR estimates with a low number of trials and high number of dimensions, as is typical for BMI applications.

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.<br><br> 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:<br><br> <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.<br> <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.<br> <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. / Ein Brain-Computer Interface (BCI) ist eine unidirektionale Schnittstelle zwischen Mensch und Computer, bei der ein Mensch in der Lage ist, ein Gerät einzig und allein Kraft seiner Gehirnsignale zu steuern. In den BCI Systemen fast aller Forschergruppen wird der Mensch in Experimenten über Wochen oder sogar Monaten trainiert, geeignete Signale zu produzieren, die vordefinierten allgemeinen Gehirnmustern entsprechen. Die BCI Gruppe in Berlin und Potsdam, der ich angehöre, war in diesem Feld eine der ersten, die erkannt hat, dass eine Anpassung des Verarbeitungssystems an den Menschen mit Hilfe der Techniken des Maschinellen Lernens große Vorteile mit sich bringt. In unserer Gruppe und mittlerweile auch in vielen anderen Gruppen wird BCI somit als aktuelles Forschungsthema im Maschinellen Lernen und folglich in der Informatik mit interdisziplinärer Natur in Neurowissenschaften und anderen Feldern verstanden, da durch die geeignete Kombination von Techniken des Maschinellen Lernens und der Signalverarbeitung basierend auf neurophysiologischem Wissen der größte Erfolg erzielt werden konnte.<br><br> In dieser Arbeit gehe ich auf meinem Anteil an diesem Projekt ein, der vor allem im Informatikbereich der BCI Forschung liegt. Im Detail beschäftige ich mich mit den folgenden drei Punkten:<br><br> <b>Diskussion eines informationstheoretischen Maßes für die Güte eines BCI's:</b> Ich habe kritisch die Annahmen von Shannon's Informationsübertragungsrate für die Anwendung im BCI Kontext beleuchtet. Durch Ermittlung von geeigneten Kodierungsstrategien konnte ich zeigen, dass dieses theoretische Maß den praktisch erreichbaren Wert ziemlich gut annähert.<br> <b>Transfer und Entwicklung von geeigneten Techniken aus dem Bereich der Signalverarbeitung und des Maschinellen Lernens:</b> Eine substantielle Komponente meiner Arbeit war die Entwicklung von Techniken des Machinellen Lernens und der Signalverarbeitung, um die Effizienz eines BCI's zu erhöhen. Basierend auf dem neurophysiologischem Wissen, dass verschiedene unabhängige Merkmale in Gehirnsignalen für verschiedene mentale Zustände beobachtbar sind, habe ich eine Methode zur Kombination von verschiedenen und unter Umständen unabhängigen Merkmalen entwickelt, die sehr erfolgreich die Fähigkeiten eines BCI's verbessert. Besonders in einigen Fällen übertraf die Leistung des entwickelten Kombinationsalgorithmus die beste Leistung auf den einzelnen Merkmalen mit mehr als 50 %. Weiterhin habe ich theoretisch und praktisch durch Einführung geeigneter Algorithmen die Frage untersucht, wie viele Klassen man für ein BCI nutzen kann und sollte. Auch hier wurde ein relevantes Resultat erzielt, nämlich dass für BCI Güten, die bis heute berichtet sind, die Benutzung von 3 oder 4 verschiedenen mentalen Zuständen in der Regel optimal im Sinne von erreichbarer Leistung sind. Für eine andere Erweiterung wurden Ideen aus der Signalverarbeitung mit denen des Maschinellen Lernens kombiniert, da ein hoher Erfolg erzielt werden kann, wenn der temporale Filter, d.h. die Wahl des benutzten Frequenzbandes, automatisch und individuell für jeden Menschen angepasst wird.<br> <b>Implementation des Berlin Brain-Computer Interfaces und Realisierung von geeigneten Experimenten:</b> Eine weitere wichtige Komponente meiner Arbeit war eine Realisierung eines online BCI Systems, welches die entwickelten Methoden umfasst, aber auch so flexibel ist, dass neue Algorithmen und Ideen einfach zu verwirklichen sind. Bis jetzt wurden mit diesem System Bitraten von bis zu 40 Bits pro Minute von absolut untrainierten Personen in ihren ersten BCI Experimenten erzielt. Dieses Resultat übertrifft die bisher berichteten Ergebnisse aller anderer BCI Gruppen deutlich. <br> <hr> Bemerkung:<br> Der Autor wurde mit dem <i>Michelson-Preis</i> 2005/2006 für die beste Promotion des Jahrgangs der Mathematisch-Naturwissenschaftlichen Fakultät der Universität Potsdam ausgezeichnet.