FG Maschinelles Lernen

30 Items

Recent Submissions
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models

Thomas, Armin W. ; Heekeren, Hauke R. ; Müller, Klaus-Robert ; Samek, Wojciech (2019-12-10)

The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size, and complex temporo-spatial dependency structure of these data. Even further, DL models often act as black boxes, impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framewor...

Evaluation of a Compact Hybrid Brain-Computer Interface System

Shin, Jaeyoung ; Müller, Klaus-Robert ; Schmitz, Christoph H. ; Kim, Do-Won ; Hwang, Han-Jeong (2017-03-08)

We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects’ forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involve...

Brain Oscillations and Functional Connectivity during Overt Language Production

Ewald, Arne ; Aristei, Sabrina ; Nolte, Guido ; Rahman, Rasha Abdel (2012-06-07)

In the present study we investigate the communication of different large scale brain sites during an overt language production task with state of the art methods for the estimation of EEG functional connectivity. Participants performed a semantic blocking task in which objects were named in semantically homogeneous blocks of trials consisting of members of a semantic category (e.g., all objects...

Powerful Statistical Inference for Nested Data Using Sufficient Summary Statistics

Dowding, Irene ; Haufe, Stefan (2018-03-19)

Hierarchically-organized data arise naturally in many psychology and neuroscience studies. As the standard assumption of independent and identically distributed samples does not hold for such data, two important problems are to accurately estimate group-level effect sizes, and to obtain powerful statistical tests against group-level null hypotheses. A common approach is to summarize subject-lev...

The Beat to Read: A Cross-Lingual Link between Rhythmic Regularity Perception and Reading Skill

Bekius, Annike ; Cope, Thomas E. ; Grube, Manon (2016-08-31)

This work assesses one specific aspect of the relationship between auditory rhythm cognition and language skill: regularity perception. In a group of 26 adult participants, native speakers of 11 different native languages, we demonstrate a strong and significant correlation between the ability to detect a “roughly” regular beat and rapid automatized naming (RAN) as a measure of language skill (...

Toward a Wireless Open Source Instrument: Functional Near-infrared Spectroscopy in Mobile Neuroergonomics and BCI Applications

von Lühmann, Alexander ; Herff, Christian ; Heger, Dominic ; Schultz, Tanja (2015-11-12)

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 mobil...

Distributed functions of detection and discrimination of vibrotactile stimuli in the hierarchical human somatosensory system

Kim, Junsuk ; Müller, Klaus-Robert ; Chung, Yoon Gi ; Chung, Soon-Cheol ; Park, Jang-Yeon ; Bülthoff, Heinrich H. ; Kim, Sung-Phil (2015-01-21)

According to the hierarchical view of human somatosensory network, somatic sensory information is relayed from the thalamus to primary somatosensory cortex (S1), and then distributed to adjacent cortical regions to perform further perceptual and cognitive functions. Although a number of neuroimaging studies have examined neuronal activity correlated with tactile stimuli, comparatively less atte...

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 ...

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...

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...

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. ...

Features and machine learning systems for structured and sequential data

Schwenk, Guido (2019)

Modern web and communication technology relies heavily on sequential and structured data for its process execution and communication protocols. Due to its complex properties, a manual analysis and detection of problems on this data is too time-consuming and expensive, and hence not feasible. As a consequence, features and automatic learning systems on this type of data are highly sought after. ...

One-class classification in the presence of point, collective, and contextual anomalies

Görnitz, Nico (2019)

Anomaly detection has a prominent position in the processing pipeline of any real-world data-driven application. Its central goal is to detect and separate valid data points from malicious-anomalous-ones such that the cleaned data set can be processed further. In many applications, anomalies are even the prime objects of interest and need to be exposed early in order to avoid loss, e.g. in cred...

Opening the machine learning black box with Layer-wise Relevance Propagation

Lapuschkin, Sebastian (2019)

Machine learning techniques such as (Deep) Neural Networks are successfully solving a plethora of tasks, e.g. in image recognition and text analysis, and provide novel predictive models for complex physical, biological and chemical systems. However, due to the nested complex and non-linear structure of many machine learning models, this comes with the disadvantage of them acting as a black box,...

Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

Laskov, Pavel ; Schäfer, Christin ; Kotenko, Igor ; Müller, Klaus-Robert (2004)

The anomaly detection methods are receiving growing attention in the intrusion detection community. The two main reasons for this are their ability to handle large volumes of unlabeled data and to detect previously unknown attacks. In this contribution we investigate the application of a modern machine learning technique – one-class Support Vector Machines (SVM) – for anomaly detection in unlab...

Multimodal instrumentation and methods for neurotechnology out of the lab

Lühmann, Alexander von (2018)

In neuroscience and related fields, progress in instrumentation, computational power, and signal processing methods continuously provide novel and increasingly powerful tools toward the investigation of brain activity in real-time and everyday environments. Research into real-life and application-oriented, non-invasive neurotechnology bears a number of multidisciplinary challenges which need to...

Functional regression of densities with application to the simulation of molecular dynamics

Brockherde, Felix (2018)

Applications of machine learning have shown promising results modeling the non-interacting kinetic energy functional in 1-D. This holds the promise of enabling orbital-free density functional theory calculations, by-passing the computationally expensive Kohn-Sham equations. This would yield substantial savings in computer-time so that larger systems or longer time scales can be simulated. ...

Large-scale approximate EM-style learning and inference in generative graphical models for sparse coding

Shelton, Jacquelyn Ann (2018)

We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alternate between learning a `selection function' to reveal the relevant latent variables, and using this to obtain a compact approximation of the posterior distribution for EM; thi...

Learning representations of atomistic systems with deep neural networks

Schütt, Kristof (2018)

Learning Representations of Atomistic Systems with Deep Neural Networks Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio. However, with the rise of applying machine learning to quantum chemistry, research has been largely focused on the development of hand-crafted descriptors of atomistic systems. In this thesis, we propose novel n...