Thorsten Dickhaus

Publications of Thorsten Dickhaus

• Psychological predictors of SMR-BCI performance.

  Authors: Eva Maria Hammer, Sebastian Halder, Benjamin Blankertz, Claudia Sannelli, Thorsten Dickhaus, Sonja Kleih, Klaus-Robert Müller, Andrea Kübler

  Biological psychology. 09/2011; 89(1):80-6.

  After about 30 years of research on Brain-Computer Interfaces (BCIs) there is little knowledge about the phenomenon, that some people - healthy as well as individuals with disease - are not able toAfter about 30 years of research on Brain-Computer Interfaces (BCIs) there is little knowledge about the phenomenon, that some people - healthy as well as individuals with disease - are not able to learn BCI-control. To elucidate this "BCI-inefficiency" phenomenon, the current study investigated whether psychological parameters, such as attention span, personality or motivation, could predict performance in a single session with a BCI controlled by modulation of sensorimotor rhythms (SMR) with motor imagery. A total of N=83 healthy BCI novices took part in the session. Psychological parameters were measured with an electronic test-battery including clinical, personality and performance tests. Predictors were determined by binary logistic regression analyses. The output variable of the Two-Hand Coordination Test (2HAND) "overall mean error duration" which is a measure for the accuracy of fine motor skills accounted for 11% of the variance in BCI-inefficiency. The Attitudes Towards Work (AHA) test variable "performance level" which can be interpreted as a degree of concentration and a neurophysiological SMR predictor were also identified as significant predictors of SMR BCI performance. Psychological parameters as measured in this study play a moderate role for one-session performance in a BCI controlled by modulation of SMR.

• Introduction to machine learning for brain imaging.

  Authors: Steven Lemm, Benjamin Blankertz, Thorsten Dickhaus, Klaus-Robert Müller

  NeuroImage. 05/2011; 56(2):387-99.

  Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental forMachine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences.

• Epigenetic quantification of tumor-infiltrating T-lymphocytes.

  Authors: Jalid Sehouli, Christoph Loddenkemper, Tatjana Cornu, Tim Schwachula, Ulrich Hoffmüller, Andreas Grützkau, Philipp Lohneis, Thorsten Dickhaus, Jörn Gröne, Martin Kruschewski, Alexander Mustea, Ivana Turbachova, Udo Baron, Sven Olek

  Epigenetics : official journal of the DNA Methylation Society. 02/2011; 6(2):236-46.

  The immune system plays a pivotal role in tumor establishment. However, the role of T-lymphocytes within the tumor microenvironment as major cellular component of the adaptive effector immuneThe immune system plays a pivotal role in tumor establishment. However, the role of T-lymphocytes within the tumor microenvironment as major cellular component of the adaptive effector immune response and their counterpart, regulatory T-cells (Treg), responsible for suppressive immune modulation, is not completely understood. This is partly due to the lack of reliable technical solutions for specific cell quantification in solid tissues. Previous reports indicated that epigenetic marks of immune cells, such as the Treg specifically demethylated region (TSDR) within the FOXP3 gene, may be exploited as robust analytical tool for Treg-quantification. Here, we expand the concept of epigenetic immunophenotyping to overall T-lymphocytes (oTL). This tool allows cell quantification with at least equivalent precision to FACS and is adoptable for analysis of blood and solid tissues. Based on this method, we analyse the frequency of Treg, oTL and their ratio in independent cohorts of healthy and tumorous ovarian, colorectal and bronchial tissues with 616 partly donor-matched samples. We find a shift of the median ratio of Treg-to-oTL from 3-8% in healthy tissue to 18-25% in all tumor entities. Epigenetically determined oTL frequencies correlate with the outcome of colorectal and ovarian cancers. Together, our data show that the composition of immune cells in tumor microenvironments can be quantitatively assessed by epigenetic measurements. This composition is disturbed in solid tumors, indicating a fundamental mechanism of tumor immune evasion. Epigenetic quantification of T-lymphocytes serves as independent clinical parameter for outcome prognosis.

• Large-scale EEG/MEG source localization with spatial flexibility.

  Authors: Stefan Haufe, Ryota Tomioka, Thorsten Dickhaus, Claudia Sannelli, Benjamin Blankertz, Guido Nolte, Klaus-Robert Müller

  NeuroImage. 01/2011; 54(2):851-9.

  We propose a novel approach to solving the electro-/magnetoencephalographic (EEG/MEG) inverse problem which is based upon a decomposition of the current density into a small number of spatial basisWe propose a novel approach to solving the electro-/magnetoencephalographic (EEG/MEG) inverse problem which is based upon a decomposition of the current density into a small number of spatial basis fields. It is designed to recover multiple sources of possibly different extent and depth, while being invariant with respect to phase angles and rotations of the coordinate system. We demonstrate the method's ability to reconstruct simulated sources of random shape and show that the accuracy of the recovered sources can be increased, when interrelated field patterns are co-localized. Technically, this leads to large-scale mathematical problems, which are solved using recent advances in convex optimization. We apply our method for localizing brain areas involved in different types of motor imagery using real data from Brain-Computer Interface (BCI) sessions. Our approach based on single-trial localization of complex Fourier coefficients yields class-specific focal sources in the sensorimotor cortices.

• Neurophysiological predictor of SMR-based BCI performance.

  Authors: Benjamin Blankertz, Claudia Sannelli, Sebastian Halder, Eva M Hammer, Andrea Kübler, Klaus-Robert Müller, Gabriel Curio, Thorsten Dickhaus

  NeuroImage. 03/2010; 51(4):1303-9.

  Brain-computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30years of BCI research, the successBrain-computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serve two purposes: a better understanding of the 'BCI-illiteracy phenomenon', and avoidance of a costly and eventually frustrating training procedure for participants who might not obtain BCI control. Furthermore, such predictors may lead to approaches to antagonize BCI illiteracy. Here, we propose a neurophysiological predictor of BCI performance which can be determined from a two minute recording of a 'relax with eyes open' condition using two Laplacian EEG channels. A correlation of r=0.53 between the proposed predictor and BCI feedback performance was obtained on a large data base with N=80 BCI-naive participants in their first session with the Berlin brain-computer interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs).

• On optimal channel configurations for SMR-based brain-computer interfaces.

  Authors: Claudia Sannelli, Thorsten Dickhaus, Sebastian Halder, Eva-Maria Hammer, Klaus-Robert Müller, Benjamin Blankertz

  Brain topography. 02/2010; 23(2):186-93.

  One crucial question in the design of electroencephalogram (EEG)-based brain-computer interface (BCI) experiments is the selection of EEG channels. While a setup with few channels is more convenientOne crucial question in the design of electroencephalogram (EEG)-based brain-computer interface (BCI) experiments is the selection of EEG channels. While a setup with few channels is more convenient and requires less preparation time, a dense placement of electrodes provides more detailed information and henceforth could lead to a better classification performance. Here, we investigate this question for a specific setting: a BCI that uses the popular CSP algorithm in order to classify voluntary modulations of sensorimotor rhythms (SMR). In a first approach 13 different fixed channel configurations are compared to the full one consisting of 119 channels. The configuration with 48 channels results to be the best one, while configurations with less channels, from 32 to 8, performed not significantly worse than the best configuration in cases where only few training trials are available. In a second approach an optimal channel configuration is obtained by an iterative procedure in the spirit of stepwise variable selection with nonparametric multiple comparisons. As a surprising result, in the second approach a setting with 22 channels centered over the motor areas was selected. Thanks to the acquisition of a large data set recorded from 80 novice participants using 119 EEG channels, the results of this study can be expected to have a high degree of generalizability.

• Localization of class-related mu-rhythm desynchronization in motor imagery based Brain-Computer Interface sessions

  Authors: Stefan Haufe, Ryota Tomioka, Thorsten Dickhaus, Claudia Sannelli, Benjamin Blankertz, Guido Nolte, Klaus-Robert Müller

  01/2010

• Localization of class-related mu-rhythm desynchronization in motor imagery based brain-computer interface sessions.

  Authors: Stefan Haufe, Ryota Tomioka, Thorsten Dickhaus, Claudia Sannelli, Benjamin Blankertz, Guido Nolte, Klaus-Robert Muller

  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 01/2010; 2010:5137-40.

  We localize the sources of class-dependent event-related desynchronisation (ERD) of the mu-rhythm related to different types of motor imagery in Brain-Computer Interfacing (BCI) sessions. OurWe localize the sources of class-dependent event-related desynchronisation (ERD) of the mu-rhythm related to different types of motor imagery in Brain-Computer Interfacing (BCI) sessions. Our approach is based on localization of single-trial Fourier coefficients using sparse basis field expansions (S-FLEX). The analysis reveals focal sources in the sensorimotor cortices, a finding which can be regarded as a proof for the expected neurophysiological origin of the BCI control signal. As a technical contribution, we extend S-FLEX to the multiple measurement case in a way that the activity of different frequency bins within the mu-band is coherently localized.

• Predicting BCI performance to study BCI illiteracy

  Authors: Thorsten Dickhaus, Claudia Sannelli, Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz

  BMC Neuroscience. 01/2009;