Inst. Softwaretechnik und Theoretische Informatik

296 Items

Recent Submissions
Enhanced Classification Methods for the Depth of Cognitive Processing Depicted in Neural Signals

Nicolae, Irina-Emilia ; Acqualagna, Laura ; Neagu, Georgeta-Mihaela ; Strungaru, Rodica ; Blankertz, Benjamin (2018)

Analyzing brain states is a difficult problem due to high variability between subjects and trials, therefore improved techniques are requested to be developed for a better discrimination between the neural components. This paper investigates multiple enhanced classification methods for neurological feature selection and discrimination of the depth of cognitive processing. The aim is to detect t...

Presenting a Spatial-Geometric EEG Feature to Classify BMD and Schizophrenic Patients

Alimardani, Fatemeh ; Boostani, Reza ; Blankertz, Benjamin (2016)

Schizophrenia (SZ) and bipolar mood disorder (BMD) patients demonstrate some similar signs and symptoms; therefore, distinguishing those using qualitative criteria is not an easy task especially when these patients experience manic or hallucination phases. This study is aimed at classifying these patients by spatial analysis of their electroencephalogram (EEG) signals. In this way, 22-channels ...

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Sannelli, Claudia ; Vidaurre, Carmen ; Müller, Klaus-Robert ; Blankertz, Benjamin (2016-05-17)

Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain–computer interfacing (BCI) when using features from spont...

Multiscale temporal neural dynamics predict performance in a complex sensorimotor task

Samek, Wojciech ; Blythe, Duncan A. J. ; Curio, Gabriel ; Klaus-Robert, Müller ; Blankertz, Benjamin ; Nikulin, Vadim V. (2016-07-09)

Ongoing neuronal oscillations are pivotal in brain functioning and are known to influence subjects' performance. This modulation is usually studied on short time scales whilst multiple time scales are rarely considered. In our study we show that Long-Range Temporal Correlations (LRTCs) estimated from the amplitude of EEG oscillations over a range of time-scales predict performance in a complex ...

ECoG high gamma activity reveals distinct cortical representations of lyrics passages, harmonic and timbre-related changes in a rock song

Sturm, Irene ; Blankertz, Benjamin ; Potes, Cristhian ; Schalk, Gerwin ; Curio, Gabriel (2014-10-13)

Listening to music moves our minds and moods, stirring interest in its neural underpinnings. A multitude of compositional features drives the appeal of natural music. How such original music, where a composer's opus is not manipulated for experimental purposes, engages a listener's brain has not been studied until recently. Here, we report an in-depth analysis of two electrocorticographic (ECoG...

Inferring network properties of cortical neurons with synaptic coupling and parameter dispersion

Roy, Dipanjan ; Jirsa, Viktor (2013-03-26)

Computational models at different space-time scales allow us to understand the fundamental mechanisms that govern neural processes and relate uniquely these processes to neuroscience data. In this work, we propose a novel neurocomputational unit (a mesoscopic model which tell us about the interaction between local cortical nodes in a large scale neural mass model) of bursters that qualitatively...

Computational models of spatial representations

Weber, Simon Nikolaus (2019)

Our sense of location depends on a structure deep within the brain called the hippocampal formation, and on its neighbor, the parahippocampal region. Studies in rodents have shown that these brain areas contain different types of neurons that help the animal to work out where it is. The activity of those neurons is modulated by the trajectory of the animal, and this modulation varies among cell...

Improving the analysis of near-infrared spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation

Gemignani, Jessica ; Middell, Eike ; Barbour, Randall L. ; Graber, Harry L. ; Blankertz, Benjamin (2018-05-09)

Objective. The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model...

Optimizing end-to-end machine learning pipelines for model training

Kunft, Andreas (2019)

Modern data analysis programs often consist of complex operations. They combine multiple heterogeneous data sources, perform data cleaning and feature transformations, and apply machine learning algorithms to train models on the preprocessed data. Existing systems can execute such end-to-end training pipelines. However, they face unique challenges in their applicability to large scale data. In ...

Formal Verification of Low-Level Code in a Model-Based Refinement Process (Technical Report: Isabelle/HOL formalization)

Berg, Nils ; Bartels, Björn ; Danziger, Armin ; Grochau Azzi, Guilherme ; Bentert, Matthias (2019-09)

This is the technical report for the Isabelle/HOL formalization accompanying the dissertation of Nils Berg. For explanations with regard to content please refer to the dissertation. The intention of this document is to give a mapping from the formalization in the dissertation to the formalization in Isabelle/HOL. Formalized are the parts where user interaction is required, i.e., the first part ...

Digital Appendix for the Dissertation 'Conception and Evaluation of E-Learning Units regarding Motivation and Acquired Competencies for Theoretical Computer Science at University Level'

Wilhelm-Weidner, Arno (2019-09-10)

This submission contains research data and analysis results of a dissertation. For this dissertation, studies were conducted in six university courses where e-learning units were used as a supplement. The aim of this research approach was mainly to find out more about the influences of the approach on motivation and competencies of the participating students.

Safety Aspects, Tolerability and Modeling of Retinofugal Alternating Current Stimulation

Haberbosch, Linus ; Datta, Abhishek ; Thomas, Chris ; Jooß, Andreas ; Köhn, Arvid ; Rönnefarth, Maria ; Scholz, Michael ; Brandt, Stephan A. ; Schmidt, Sein (2019-08-07)

Background While alternating current stimulation (ACS) is gaining relevance as a tool in research and approaching clinical applications, its mechanisms of action remain unclear. A review by Schutter and colleagues argues for a retinal origin of transcranial ACS’ neuromodulatory effects. Interestingly, there is an alternative application form of ACS specifically targeting α-oscillations in the ...

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses

Shin, Jaeyoung ; Kim, Do-Won ; Müller, Klaus-Robert ; Hwang, Han-Jeong (2018-06-05)

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vuln...

Efficient learning machines

Alber, Maximilian (2019)

Science is in a constant state of evolution. There is a permanent quest for advancing knowledge in the light of changing capabilities and matters. The field of Machine Learning itself is shaped by the ever-increasing amount of data and computing power, creating new challenges as well as paving the way for new opportunities. This thesis is on adapting learning-based machines to these emerging pr...

Konzeption eines MOOC der TU9 zum Thema Communication Acoustics

Möller, Sebastian ; Ahrens, Jens ; Altinsoy, Ercan ; Fels, Janina ; Müller, Gerhard ; Reimers, Gabriel ; Seeber, Bernhard ; Vorländer, Michael ; Weinzierl, Stefan (2016)

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On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Choi, Soo-In ; Han, Chang-Hee ; Choi, Ga-Young ; Shin, Jaeyoung ; Song, Kwang Soup ; Im, Chang-Hwan ; Hwang, Han-Jeong (2018-08-29)

Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main e...

Co-Clustering under the Maximum Norm

Bulteau, Laurent ; Froese, Vincent ; Hartung, Sepp ; Niedermeier, Rolf (2016-02-25)

Co-clustering, that is partitioning a numerical matrix into “homogeneous” submatrices, has many applications ranging from bioinformatics to election analysis. Many interesting variants of co-clustering are NP-hard. We focus on the basic variant of co-clustering where the homogeneity of a submatrix is defined in terms of minimizing the maximum distance between two entries. In this context, we sp...

Finding Supported Paths in Heterogeneous Networks

Fertin, Guillaume ; Komusiewicz, Christian ; Mohamed-Babou, Hafedh ; Rusu, Irena (2015-10-09)

Subnetwork mining is an essential issue in the analysis of biological, social and communication networks. Recent applications require the simultaneous mining of several networks on the same or a similar vertex set. That is, one searches for subnetworks fulfilling different properties in each input network. We study the case that the input consists of a directed graph D and an undirected graph G...

Towards exact molecular dynamics simulations with invariant machine-learned models

Chmiela, Stefan (2019)

Molecular dynamics (MD) simulations constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, one of the widely recognized and increasingly pressing issues in MD simulations is the lack of accuracy of underlying classical interatomic potentials, which hinders truly predictive modeling of dynamics and function of (bio)molecular systems. ...

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes

Donner, Christian ; Opper, Manfred (2018)

We present an approximate Bayesian inference approach for estimating the intensity of a inhomogeneous Poisson process, where the intensity function is modelled using a Gaussian process (GP) prior via a sigmoid link function. Augmenting the model using a latent marked Poisson process and Polya--Gamma random variables we obtain a representation of the likelihood which is conjugate to the GP prior...