Arne Ewald | Philips Electronics (original) (raw)

Papers by Arne Ewald

Brain oscillations and functional connectivity during overt

Journal of Neuroscience Methods, 2014

h i g h l i g h t s • We suggest a new normalization factor for a bispectrum which is a product o... more h i g h l i g h t s • We suggest a new normalization factor for a bispectrum which is a product of univariate quantities. The absolute value of the normalized bispectrum is proved to be bounded by zero and one using Hölders inequality. • The new normalization is compared to three widely used normalizations, specifically showing that the proposed normalization factor, in contrast to other factors leading to bounded measures, is itself not affected by coupling between the signals. • The significance of the bicoherence values of different normalization methods are compared to each other using statistical tests. We did not find evidence to conclude that the normalization has a relevant effect on the statistical power of the bicoherence values.

NeuroImage, 2015

MUltiple SIgnal Classification (MUSIC) is a standard localization method which is based on the id... more MUltiple SIgnal Classification (MUSIC) is a standard localization method which is based on the idea of dividing the vector space of the data into two subspaces: signal subspace and noise subspace. The brain, divided into several grid points, is scanned entirely and the grid point with the maximum consistency with the signal subspace is considered as the source location. In one of the MUSIC variants called Recursively Applied and Projected MUSIC (RAP-MUSIC), multiple iterations are proposed in order to decrease the location estimation uncertainties introduced by subspace estimation errors. In this paper, we suggest a new method called Self-Consistent MUSIC (SC-MUSIC) which extends RAP-MUSIC to a self-consistent algorithm. This method, SC-MUSIC, is based on the idea that the presence of several sources has a bias on the localization of each source. This bias can be reduced by projecting out all other sources mutually rather than iteratively. While the new method is applicable in all situations when MUSIC is applicable we will study here the localization of interacting sources using the imaginary part of the cross-spectrum due to the robustness of this measure to the artifacts of volume conduction. For an odd number of sources this matrix is rank deficient similar to covariance matrices of fully correlated sources. In such cases MUSIC and RAP-MUSIC fail completely while the new method accurately localizes all sources. We present results of the method using simulations of odd and even number of interacting sources in the presence of different noise levels. We compare the method with three other source localization methods: RAP-MUSIC, dipole fit and MOCA (combined with minimum norm estimate) through simulations. SC-MUSIC shows substantial improvement in the localization accuracy compared to these methods. We also show results for real MEG data of a single subject in the resting state. Four sources are localized in the sensorimotor area at f=11Hz which is the expected region for the idle rhythm.

Neuroscience Letters, 2011

Frontiers in Psychology, 2012

IEEE Transactions on Biomedical Engineering

Objective: Electrical source imaging of brain activity is most accurate when using individualized... more Objective: Electrical source imaging of brain activity is most accurate when using individualized bioelectric head models. Constructing these models requires identifying electrode positions on the scalp surface. Current methods such as photogrammetry involve significant user interaction that limits integration in clinical workflows. This work introduces and validates a new, fully-automatic method for sensor registration. Methods: Average electrode coordinates are registered to the mean scalp mesh of a shape-constrained deformable head model used for tissue segmentation. Patient-specific electrode positions can be identified on the deformed scalp surface using point-based correspondence after model adaptation. Results: The performance of the proposed method for sensor registration is evaluated with simulated and real data. Electrode variability is quantified for a photogrammetry-based solution and compared against the proposed sensor registration. Conclusion: A fully-automated model-based approach can identify electrode locations with similar accuracy as a current state-of-the-art photogrammetry system. Significance: The new method for sensor registration presented in this work is rapid and fully automatic. It eliminates any user dependent inaccuracy introduced in sensor registration and ensures reproducible results. More importantly, it can more easily be integrated in clinical workflows, enabling broader adoption of electrical source imaging technologies.

Die Funktionsweise des Gehirns wird maßgeblich durch das Zusammenspiel von verschiedenen Gehirnre... more Die Funktionsweise des Gehirns wird maßgeblich durch das Zusammenspiel von verschiedenen Gehirnregionen bestimmt. Ein Kommunikationsmechanismus ist dabei die Synchronisation von oszillatorischen Signalen, die von großen Neuronenpopulationen erzeugt werden. Mit ihrer hervorragenden zeitlichen Auflösung im Bereich von Millisekunden und ihrer Nicht-Invasivität sind die elektrophysiologischen Messmodalitäten Elektroenzephalografie (EEG) und Magnetoenzephalografie (MEG) gut geeignete Werkzeuge, um diese Synchronisationseffekte zu untersuchen. Die Sensoren an der Kopfoberfläche messen jedoch eine Mischung der im Gehirn generierten Quellsignale. Durch diesen sogenannten Volumenleitungseffekt ist es weder möglich, die Quellen aus den Sensorsignalen eindeutig zu rekonstruieren noch Beziehungen (Konnektivität) zwischen ihnen abzuleiten. Die vorliegende Arbeit stellt ein Reihe multivariater Datenanalysemethoden vor, um Synchronisation und damit Interaktionen zwischen verschiedenen Gehirnareale...

To understand the functionality of the brain, it is crucial to understand the interplay between s... more To understand the functionality of the brain, it is crucial to understand the interplay between sources of ongoing activity inside the brain. The synchronization of oscillatory signals generated by large populations of neurons has been identified to serve as a communication mechanism. Due to their excellent temporal resolution in the millisecond range and their non-invasiveness, the electrophysiological measurement modalities Electroencephalography (EEG) and Magnetoencephalography (MEG) are well-suited tools to investigate these synchronization effects. However, due to the mixing of source signals inside the brain into measurement sensors outside the head which is called volume conduction, it is neither possible to uniquely reconstruct the sources nor to study relationships among them. Artifacts of volume conduction impede the interpretation of relationships between sensors as well as between estimated sources. Within this thesis a novel series of multivariate data analysis methods ...

Brain topography, Jan 2, 2016

Due to its high temporal resolution, electroencephalography (EEG) is widely used to study functio... more Due to its high temporal resolution, electroencephalography (EEG) is widely used to study functional and effective brain connectivity. Yet, there is currently a mismatch between the vastness of studies conducted and the degree to which the employed analyses are theoretically understood and empirically validated. We here provide a simulation framework that enables researchers to test their analysis pipelines on realistic pseudo-EEG data. We construct a minimal example of brain interaction, which we propose as a benchmark for assessing a methodology's general eligibility for EEG-based connectivity estimation. We envision that this benchmark be extended in a collaborative effort to validate methods in more complex scenarios. Quantitative metrics are defined to assess a method's performance in terms of source localization, connectivity detection and directionality estimation. All data and code needed for generating pseudo-EEG data, conducting source reconstruction and connectivi...

NeuroImage, 2014

We introduce a novel method to estimate bivariate synchronization, i.e. interacting brain sources... more We introduce a novel method to estimate bivariate synchronization, i.e. interacting brain sources at a specific frequency or band, from MEG or EEG data robust to artifacts of volume conduction. The data driven calculation is solely based on the imaginary part of the cross-spectrum as opposed to the imaginary part of coherency. In principle, the method quantifies how strong a synchronization between a distinct pair of brain sources is present in the data. As an input of the method all pairs of pre-defined locations inside the brain can be used which is computationally exhaustive. In contrast to that, reference sources can be used that have been identified by any source reconstruction technique in a prior analysis step. We introduce different variants of the method and evaluate the performance in simulations. As a particular advantage of the proposed methodology, we demonstrate that the novel approach is capable of investigating differences in brain source interactions between experim...

(FEM) has been adapted to solve optical (via NAVI) or electromagnetic (via EMSE) forward problems... more (FEM) has been adapted to solve optical (via NAVI) or electromagnetic (via EMSE) forward problems. EMSE provides users with a tool for segmentation and FEM mesh generation from individual or standard MRIs. Fig.2 shows FEM meshs generated for EEG and Optical compuations. [Left], a whole-head FEM mesh suitable for EEG source reconstruction techniques. [Right], coronal view of a mesh situated beneath an optode array using a standard (MNI average) MRI. A Computing Environment for Multimodal Integration of EEG and fNIRS

International Journal of Psychophysiology, 2012

Frontiers in Neuroscience, 2010

ABSTRACT The major problem in examining interactions between sources of brain activity from MEG a... more ABSTRACT The major problem in examining interactions between sources of brain activity from MEG and EEG data are the ‘artifacts of volume conduction’ denoting the fact that the sensors measure a largely unknown superposition of brain activities. In this work we suggest a method to test for artifacts of volume conductions. The test is absolutely general: it is applicable to both linear and nonlinear methods and both on the sensor and source level. The idea of the method is to construct surrogate data which are statistically as close as possible to the original data but which are a superposition of independent sources. As a first step we decompose the data via Independent Component Analysis (ICA) resulting in signals which are as independent as possible. In a second step we destroy any remaining dependencies by shifting the n.th signal by a time (n-1)*T where T must be substantially larger than any autocorrelation time. Finally, these shifted signals are mixed again into sensors using the mixing matrix found by the ICA algorithm. Any interaction measure can now be calculated both for the original and the surrogate data. If a specific effect can be seen in both data sets we consider this effect as insufficient evidence for an observed brain interaction. We applied this method to real and imaginary parts of coherency (real EEG data), 1:2 phase locking (real EEG data) and Granger causality (simulated data). We found that the real part of coherency is almost perfectly consistent with mixtures of independent sources while the imaginary part cannot be explained with the surrogate data at all. Results for phase locking can only be explained partly by volume conductions. For true directional interactions Granger causality is attenuated but not removed in the surrogate data as compared to the original data. KeywordsFunctional connectivity measures-Volume conduction-Surrogate Data-Coherency method-Phase Locking-Granger causality-ICA

Frontiers in Computational Neuroscience, 2010

ABSTRACT Most EEG/MEG based Brain Computer Interfaces (BCI) employ machine learning techniques to... more ABSTRACT Most EEG/MEG based Brain Computer Interfaces (BCI) employ machine learning techniques to discriminate and classify the recorded data belonging to different classes. Usually, no neurophysiological knowledge is used within the classification algorithms. Here, a method is proposed that includes prior knowledge about the locations of sources of imagined movement of the left and the right hand by projecting EEG/MEG data onto a subspace defined by modeled sources at the corresponding locations in somatosensory areas. Three different source models are investigated. First, one radial dipole on each side is based on the assumption that both location and orientation are known. Hence, for two sides, a 2-dimensional subspace is selected. Second, three dipoles at each location span a 6-dimensional subspace assuming known locations but uncertain orientations. Third, we modeled the sources as multipoles up to quadrupolar order resulting in a 16-dimensional subspace. The multipole expansion systematically corrects for inaccuracies both in location and exact shape of the source. After the projection onto respective topographies, feature extraction is performed on the reduced data by Common Spatial Filter (CSP) analysis. Finally, Linear Discriminant Analysis (LDA) is applied for classification. The projection of the data leads to a reduction of the dimensionality of the signal focusing on those parts of the signal which are generated or suppressed in the motor cortex during imagined hand movement. Since EEG/MEG data are strongly affected by various types of artifacts hampering the classification the proposed procedure leads to a removal of parts of the signal and therefore a reduction of artifacts. For EEG data it is shown that a projection with respect to source locations prior to CSP analysis leads to a gain of BCI performance when the sources are modeled as multipoles. KeywordsBCI-source modeling-multipoles-ERD

Computational and Mathematical Methods in Medicine, 2012

To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracti... more To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracting brain sources do not contribute systematically to the imaginary part of the cross-spectrum. Firstly, we propose to apply the existing subspace method “RAP-MUSIC” to the subspace found from the dominant singular vectors of the imaginary part of the cross-spectrum rather than to the conventionally used covariance matrix. Secondly, to estimate the specific sources interacting with each other, we use a modified LCMV-beamformer approach in which the source direction for each voxel was determined by maximizing the imaginary coherence with respect to a given reference. These two methods are applicable in this form only if the number of interacting sources is even, because odd-dimensional subspaces collapse to even-dimensional ones. Simulations show that (a) RAP-MUSIC based on the imaginary part of the cross-spectrum accurately finds the correct source locations, that (b) conventional RAP-MUS...

Biomedizinische Technik/Biomedical Engineering, 2013

The investigation of functional neuronal synchronization has recently become a growing field of r... more The investigation of functional neuronal synchronization has recently become a growing field of research. With high temporal resolution, electroencephalography and magnetoencephalography are well-suited measurement techniques to identify networks of interacting sources underlying the recorded data. The analysis of the data in terms of effective connectivity, nevertheless, contains intrinsic issues such as the problem of volume conduction and the non-uniqueness of the inverse solution. Here, we briefly introduce a series of existing methods assessing these problems. To determine the locations of interacting brain sources robust to volume conduction, all computations are solely based on the imaginary part of the cross-spectrum as a trustworthy source of information. Furthermore, we demonstrate the feasibility of estimating causal relationships of systems of neuronal sources with the phase slope index in realistically simulated data. Finally, advantages and drawbacks of the applied methodology are highlighted and discussed.

... Event-Related Potentials preceding Emergency Braking Situations during Simulated Driving Stef... more ... Event-Related Potentials preceding Emergency Braking Situations during Simulated Driving Stefan Haufe, Matthias Treder, Max Sagebaum, Manfred Gugler, Arne Ewald, Gabriel Curio andBenjamin Blankertz In: TOBI Workshop 2010: Integrating Brain-Computer Interfaces with ...