Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness

In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. Darüber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes für zwei innovative BCI Paradigmen, für die es bisher keine etablierte Mustererkennungsmethodik gibt

Towards a wireless open source instrument: functional Near-Infrared Spectroscopy in mobile neuroergonomics and BCI applications

Brain-Computer Interfaces (BCIs) and neuroergonomics research have high requirements regarding robustness and mobility. Additionally, fast applicability and customization are desired. Functional Near-Infrared Spectroscopy (fNIRS) is an increasingly established technology with a potential to satisfy these conditions. EEG acquisition technology, currently one of the main modalities used for mobile brain activity assessment, is widely spread and open for access and thus easily customizable. fNIRS technology on the other hand has either to be bought as a predefined commercial solution or developed from scratch using published literature. To help reducing time and effort of future custom designs for research purposes, we present our approach toward an open source multichannel stand-alone fNIRS instrument for mobile NIRS-based neuroimaging, neuroergonomics and BCI/BMI applications. The instrument is low-cost, miniaturized, wireless and modular and openly documented on www.opennirs.org. It provides features such as scalable channel number, configurable regulated light intensities, programmable gain and lock-in amplification. In this paper, the system concept, hardware, software and mechanical implementation of the lightweight stand-alone instrument are presented and the evaluation and verification results of the instrument\u27s hardware and physiological fNIRS functionality are described. Its capability to measure brain activity is demonstrated by qualitative signal assessments and a quantitative mental arithmetic based BCI study with 12 subjects

Star and dust formation activities in AzTEC-3: A starburst galaxy at z = 5.3

Analyses of high-redshift ultraluminous infrared (IR) galaxies traditionally use the observed optical to submillimeter spectral energy distribution (SED) and estimates of the dynamical mass as observational constraints to derive the star formation rate (SFR), the stellar mass, and age of these objects. An important observational constraint neglected in the analysis is the mass of dust giving rise to the IR emission. In this paper we add this constraint to the analysis of AzTEC-3. Adopting an upper limit to the mass of stars and a bolometric luminosity for this object, we construct stellar and chemical evolutionary scenarios, constrained to produce the inferred dust mass and observed luminosity before the associated stellar mass exceeds the observational limit. We find that the model with a Top Heavy IMF provided the most plausible scenario consistent with the observational constraints. In this scenario the dust formed over a period of ~200 Myr, with a SFR of ~500 Msun/yr. These values for the age and SFR in AzTEC-3 are significantly higher and lower, respectively, from those derived without the dust mass constraint. However, this scenario is not unique, and others cannot be completely ruled out because of the prevailing uncertainties in the age of the galaxy, its bolometric luminosity, and its stellar and dust masses. A robust result of our models is that all scenarios require most of the radiating dust mass to have been accreted in molecular clouds. Our new procedure highlights the importance of a multiwavelength approach, and of the use of dust evolution models in constraining the age and the star formation activity and history in galaxies.Comment: Accepted for publication in the ApJ. 12 pages with 11 embedded figure

Brain-to-text: Decoding spoken phrases from phone representations in the brain

It has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings. Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To-Text system described in this paper represents an important step toward human-machine communication based on imagined speech

Insights into hominid evolution from the gorilla genome sequence.

Gorillas are humans' closest living relatives after chimpanzees, and are of comparable importance for the study of human origins and evolution. Here we present the assembly and analysis of a genome sequence for the western lowland gorilla, and compare the whole genomes of all extant great ape genera. We propose a synthesis of genetic and fossil evidence consistent with placing the human-chimpanzee and human-chimpanzee-gorilla speciation events at approximately 6 and 10 million years ago. In 30% of the genome, gorilla is closer to human or chimpanzee than the latter are to each other; this is rarer around coding genes, indicating pervasive selection throughout great ape evolution, and has functional consequences in gene expression. A comparison of protein coding genes reveals approximately 500 genes showing accelerated evolution on each of the gorilla, human and chimpanzee lineages, and evidence for parallel acceleration, particularly of genes involved in hearing. We also compare the western and eastern gorilla species, estimating an average sequence divergence time 1.75 million years ago, but with evidence for more recent genetic exchange and a population bottleneck in the eastern species. The use of the genome sequence in these and future analyses will promote a deeper understanding of great ape biology and evolution

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Online recognition of facial actions for natural EEG-based BCI applications

Abstract. We present a system for classification of nine voluntary facial actions, i.e. Neutral, Smile, Sad, Surprise, Angry, Speak, Blink, Left, and Right. The data is assessed by an Emotiv EPOC wireless EEG head-set. We derive spectral features and step function features that represent the main signal characteristics of the recorded data in a straightforward manner. With a two stage classification setup using support vector machines we achieve an overall recognition accuracy of 81.8%. Furthermore, we show a qualitative evaluation of an online system for facial action recognition using the EPOC device