Alois Schlögl

Publications of Alois Schlögl

• BioSig: The Free and Open Source Software Library for Biomedical Signal Processing.

  Authors: Carmen Vidaurre, Tilmann Heinrich Sander, Alois Schlögl

  Comp. Int. and Neurosc. 01/2011; 2011.

• BioSig: the free and open source software library for biomedical signal processing.

  Authors: Carmen Vidaurre, Tilmann H Sander, Alois Schlögl

  Computational intelligence and neuroscience. 01/2011; 2011:935364.

  BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open sourceBioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

• Recent advances in modeling and analysis of bioelectric and biomagnetic sources.

  Authors: Tilmann H Sander, Thomas R Knösche, Alois Schlögl, Florian Kohl, Carsten H Wolters, Jens Haueisen, Lutz Trahms

  Biomedizinische Technik. Biomedical engineering. 01/2010; 55(2):65-76.

  Determining the centers of electrical activity in the human body and the connectivity between different centers of activity in the brain is an active area of research. To understand brain functionDetermining the centers of electrical activity in the human body and the connectivity between different centers of activity in the brain is an active area of research. To understand brain function and the nature of cardiovascular diseases requires sophisticated methods applicable to non-invasively measured bioelectric and biomagnetic data. As it is difficult to solve for all unknown parameters at once, several strains of data analysis have been developed, each trying to solve a different part of the problem and each requiring a different set of assumptions. Current trends and results from major topics of electro- and magnetoencephalographic data analysis are presented here together with the aim of stimulating research into the unification of the different approaches. The following topics are discussed: source reconstruction using detailed finite element modeling to locate sources deep in the brain; connectivity analysis for the quantification of strength and direction of information flow between activity centers, preferably incorporating an inverse solution; the conflict between the statistical independence assumption of sources and a possible connectivity; the verification and validation of results derived from non-invasively measured data through animal studies and phantom measurements. This list already indicates the benefits of a unified view.

• Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces.

  Authors: Carmen Vidaurre, Nicole Krämer, Benjamin Blankertz, Alois Schlögl

  Neural networks : the official journal of the International Neural Network Society. 08/2009;

  Several feature types have been used with EEG-based Brain-Computer Interfaces. Among the most popular are logarithmic band power estimates with more or less subject-specific optimization of theSeveral feature types have been used with EEG-based Brain-Computer Interfaces. Among the most popular are logarithmic band power estimates with more or less subject-specific optimization of the frequency bands. In this paper we introduce a feature called Time Domain Parameter that is defined by the generalization of the Hjorth parameters. Time Domain Parameters are studied under two different conditions. The first setting is defined when no data from a subject is available. In this condition our results show that Time Domain Parameters outperform all band power features tested with all spatial filters applied. The second setting is the transition from calibration (no feedback) to feedback, in which the frequency content of the signals can change for some subjects. We compare Time Domain Parameters with logarithmic band power in subject-specific bands and show that these features are advantageous in this situation as well.

• Robustly estimating the flow direction of information in complex physical systems.

  Authors: Guido Nolte, Andreas Ziehe, Vadim V Nikulin, Alois Schlögl, Nicole Krämer, Tom Brismar, Klaus-Robert Müller

  Physical review letters. 06/2008; 100(23):234101.

  We propose a new measure (phase-slope index) to estimate the direction of information flux in multivariate time series. This measure (a) is insensitive to mixtures of independent sources, (b) givesWe propose a new measure (phase-slope index) to estimate the direction of information flux in multivariate time series. This measure (a) is insensitive to mixtures of independent sources, (b) gives meaningful results even if the phase spectrum is not linear, and (c) properly weights contributions from different frequencies. These properties are shown in extended simulations and contrasted to Granger causality which yields highly significant false detections for mixtures of independent sources. An application to electroencephalography data (eyes-closed condition) reveals a clear front-to-back information flow.

• BIOSIG - Standardization and Quality Control in Biomedical Signal Processing Using the BioSig Project.

  Authors: Alois Schlögl, Carmen Vidaurre, Ernst Hofer, Thomas Wiener, Clemens Brunner, Reinhold Scherer, Franco Chiarugi

  Proceedings of the First International Conference on Biomedical Electronics and Devices, BIOSIGNALS 2008, Funchal, Madeira, Portugal, January 28-31, 2008, Volume 2; 01/2008

• Toward self-paced brain--computer communication: Navigation through virtual worlds

  Authors: Reinhold Scherer, Felix Lee, Alois Schlögl, Robert Leeb, Horst Bischof, Gert Pfurtscheller

  IEEE Trans. Biomed. Eng. 01/2008; 55:675--682.

• Robustly estimating the flow direction of information in complex physical systems

  Authors: Guido Nolte, Andreas Ziehe, Vadim V Nikulin, Alois Schlögl, Nicole Krämer, Tom Brismar, Klaus-Robert Müller

  12/2007;

  We propose a new measure to estimate the direction of information flux in multivariate time series from complex systems. This measure, based on the slope of the phase spectrum (Phase Slope Index) hasWe propose a new measure to estimate the direction of information flux in multivariate time series from complex systems. This measure, based on the slope of the phase spectrum (Phase Slope Index) has invariance properties that are important for applications in real physical or biological systems: (a) it is strictly insensitive to mixtures of arbitrary independent sources, (b) it gives meaningful results even if the phase spectrum is not linear, and (c) it properly weights contributions from different frequencies. Simulations of a class of coupled multivariate random data show that for truly unidirectional information flow without additional noise contamination our measure detects the correct direction as good as the standard Granger causality. For random mixtures of independent sources Granger Causality erroneously yields highly significant results whereas our measure correctly becomes non-significant. An application of our novel method to EEG data (88 subjects in eyes-closed condition) reveals a strikingly clear front-to-back information flow in the vast majority of subjects and thus contributes to a better understanding of information processing in the brain. Comment: 5 pages, 4 figures

  Download

• Study of discriminant analysis applied to motor imagery bipolar data.

  Authors: Carmen Vidaurre, Reinhold Scherer, Rafael Cabeza, Alois Schlögl, Gert Pfurtscheller

  Medical & biological engineering & computing. 02/2007; 45(1):61-8.

  We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separateWe present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA.

• Directed cortical information flow during human object recognition: analyzing induced EEG gamma-band responses in brain's source space.

  Authors: Gernot G Supp, Alois Schlögl, Nelson Trujillo-Barreto, Matthias M Müller, Thomas Gruber

  PLoS ONE. 01/2007; 2(1):e684.

  The increase of induced gamma-band responses (iGBRs; oscillations >30 Hz) elicited by familiar (meaningful) objects is well established in electroencephalogram (EEG) research. This frequency-specificThe increase of induced gamma-band responses (iGBRs; oscillations >30 Hz) elicited by familiar (meaningful) objects is well established in electroencephalogram (EEG) research. This frequency-specific change at distinct locations is thought to indicate the dynamic formation of local neuronal assemblies during the activation of cortical object representations. As analytically power increase is just a property of a single location, phase-synchrony was introduced to investigate the formation of large-scale networks between spatially distant brain sites. However, classical phase-synchrony reveals symmetric, pair-wise correlations and is not suited to uncover the directionality of interactions. Here, we investigated the neural mechanism of visual object processing by means of directional coupling analysis going beyond recording sites, but rather assessing the directionality of oscillatory interactions between brain areas directly. This study is the first to identify the directionality of oscillatory brain interactions in source space during human object recognition and suggests that familiar, but not unfamiliar, objects engage widespread reciprocal information flow. Directionality of cortical information-flow was calculated based upon an established Granger-Causality coupling-measure (partial-directed coherence; PDC) using autoregressive modeling. To enable comparison with previous coupling studies lacking directional information, phase-locking analysis was applied, using wavelet-based signal decompositions. Both, autoregressive modeling and wavelet analysis, revealed an augmentation of iGBRs during the presentation of familiar objects relative to unfamiliar controls, which was localized to inferior-temporal, superior-parietal and frontal brain areas by means of distributed source reconstruction. The multivariate analysis of PDC evaluated each possible direction of brain interaction and revealed widespread reciprocal information-transfer during familiar object processing. In contrast, unfamiliar objects entailed a sparse number of only unidirectional connections converging to parietal areas. Considering the directionality of brain interactions, the current results might indicate that successful activation of object representations is realized through reciprocal (feed-forward and feed-backward) information-transfer of oscillatory connections between distant, functionally specific brain areas.

• BCI Meeting 2005--workshop on BCI signal processing: feature extraction and translation.

  Authors: Dennis J McFarland, Charles W. Anderson, Klaus-Robert Müller, Alois Schlögl, Dean J Krusienski

  IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 07/2006; 14(2):135-8.

  This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant toThis paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.

• The BCI competition. III: Validating alternative approaches to actual BCI problems.

  Authors: Benjamin Blankertz, Klaus-Robert Müller, Dean J Krusienski, Gerwin Schalk, Jonathan R Wolpaw, Alois Schlögl, Gert Pfurtscheller, José del R. Millán, Michael Schröder, Niels Birbaumer

  IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 07/2006; 14(2):153-9.

  A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation ofA brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

• Analyzing event-related EEG data with multivariate autoregressive parameters.

  Authors: Alois Schlögl, Gernot Supp

  Progress in brain research. 02/2006; 159:135-47.

  Methods of spatio-temporal analysis provide important tools for characterizing several dynamic aspects of brain oscillations that are reflected in the human scalp-detected electroencephalogram (EEG).Methods of spatio-temporal analysis provide important tools for characterizing several dynamic aspects of brain oscillations that are reflected in the human scalp-detected electroencephalogram (EEG). The search to identify the dynamic connectivity of brain signals within different frequency bands, in order to uncover the transient cooperation between different brain sites, converges at the potential of multivariate autoregressive (MVAR) models and their derived parameters. In fact, MVAR parameters provide a whole battery of so-called coupling measures including classic coherence (COH), partial coherence (pCOH), imaginary part of coherence (iCOH), partial-directed coherence (PDC), directed transfer function (DTF), and full frequency directed transfer function (ffDTF). All of these approaches have been developed to quantify the degree of coupling between different EEG recording positions, with the specific aim to characterize the functional interaction between neural populations within the cortex. This work addresses the application of MVAR models to event-related brain processes, including different statistical approaches, and reviews most relevant findings in the expanding field of coupling analysis. Finally, we present several examples of coupling patterns associated with certain types of movement imagery.

• Characterization of four-class motor imagery EEG data for the BCI-competition 2005.

  Authors: Alois Schlögl, Felix Lee, Horst Bischof, Gert Pfurtscheller

  Journal of neural engineering. 01/2006; 2(4):L14-22.

  To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjectsTo determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)--for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst.

• Hemodynamic and autonomic changes induced by Ironman: prediction of competition time by blood pressure variability.

  Authors: Gerfried Gratze, Richard Rudnicki, Wolfgang Urban, Harald Mayer, Alois Schlögl, Falko Skrabal

  Journal of applied physiology (Bethesda, Md. : 1985). 12/2005; 99(5):1728-35.

  We hypothesized that the extreme endurance exercise of an Ironman competition would lead to long-standing hemodynamic and autonomic changes. We investigated also the possibility of predictingWe hypothesized that the extreme endurance exercise of an Ironman competition would lead to long-standing hemodynamic and autonomic changes. We investigated also the possibility of predicting competition performance from baseline hemodynamic and autonomic parameters. We have investigated 27 male athletes before competition, 1 h after, and then for the following week after the competition. The Task Force monitor was used to measure beat-to-beat hemodynamic and autonomic parameters during supine rest and active standing. Heart rate (P < 0.001) was increased, and stroke index (P = 0.011), systolic blood pressure (P = 0.004), diastolic blood pressure (P < 0.001), total peripheral resistance index (P < 0.001), and baroreceptor reflex sensitivity (P < 0.001) were decreased after the competition. The 0.05- to 0.17-Hz band of heart rate and blood pressure variability was increased (P < 0.001 and P < 0.001, respectively), the 0.17- to 0.40-Hz band of heart rate interval variability was decreased after the competition (P < 0.001). All parameters returned to baseline values 3 days after the competition. After the competition, the autonomic response to orthostasis was significantly impaired. The 0.05- to 0.17-Hz band of diastolic blood pressure variability before competition and weekly net exercise training, but not the other hemodynamic and autonomic parameters, were related to competition time in multivariate regression analysis (multiple r = 0.70, P < 0.001). The marked hemodynamic and autonomic changes after an ultraendurance race, which are compatible with myocardial depression in the face of sympathetic activation and reduction of afterload, return to baseline after only 1-3 days. Because the 0.05- to 0.17-Hz band of diastolic blood pressure variability contributes to the prediction of competition time, the analysis of blood pressure variability in the frequency domain deserves further study for the prediction of endurance capacity.

• Semantic memory retrieval: cortical couplings in object recognition in the N400 window.

  Authors: Gernot G Supp, Alois Schlögl, Christian J. Fiebach, Thomas C. Gunter, Gabriella Vigliocco, Gert Pfurtscheller, Hellmuth Petsche

  The European journal of neuroscience. 03/2005; 21(4):1139-43.

  To characterize the regional changes in neuronal couplings and information transfer related to semantic aspects of object recognition in humans we used partial-directed EEG-coherence analysis (PDC).To characterize the regional changes in neuronal couplings and information transfer related to semantic aspects of object recognition in humans we used partial-directed EEG-coherence analysis (PDC). We examined the differences of processing recognizable and unrecognizable pictures as reflected by changes in cortical networks within the time-window of a determined event-related potential (ERP) component, namely the N400. Fourteen participants performed an image recognition task, while sequentially confronted with pictures of recognizable and unrecognizable objects. The time-window of N400 as indicative of object semantics was defined from the ERP. Differences of PDC in the beta-band between these tasks were represented topographically as patterns of electrical couplings, possibly indicating changing degrees of functional cooperation between brain areas. Successful memory retrieval of picture meaning appears to be supported by networks comprising left temporal and parietal regions and bilateral frontal brain areas.

• The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.

  Authors: Benjamin Blankertz, Klaus-Robert Müller, Gabriel Curio, Theresa M Vaughan, Gerwin Schalk, Jonathan R Wolpaw, Alois Schlögl, Christa Neuper, Gert Pfurtscheller, Thilo Hinterberger, Michael Schröder, Niels Birbaumer

  IEEE transactions on bio-medical engineering. 07/2004; 51(6):1044-51.

  Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel betweenInterest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.

• Lexical memory search during N400: cortical couplings in auditory comprehension.

  Authors: Gernot G Supp, Alois Schlögl, Thomas C. Gunter, Montaine Bernard, Gert Pfurtscheller, Hellmuth Petsche

  Neuroreport. 06/2004; 15(7):1209-13.

  In this study, we applied partial-directed EEG-coherence analysis to assess regional changes in neuronal couplings and information transfer related to semantic processing. We tested the hypothesisIn this study, we applied partial-directed EEG-coherence analysis to assess regional changes in neuronal couplings and information transfer related to semantic processing. We tested the hypothesis whether (and which) processing differences between spoken words and pseudowords are reflected by changes in cortical networks within the time window of a specific event related potential (ERP) component, the N400. Fourteen native speaking German subjects performed a lexical decision paradigm, while being confronted sequentially with two-syllabic nouns and phonologically legal pseudowords. Using ERP analysis, we defined the time window of the N400 effect, known to reflect semantic processing, and, subsequently, we examined the coupling differences. Lexico-semantic memory search appears to be subserved by a network between temporal, parietal and frontal areas, particularly restricted to the left hemisphere.

• Detection of movement-related desynchronization patterns in ongoing single-channel electrocorticogram.

  Authors: Bernhard Graimann, Jane E Huggins, Alois Schlögl, Simon P Levine, Gert Pfurtscheller

  IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 10/2003; 11(3):276-81.

  Adaptive autoregressive parameters and a linear classifier were used to detect movement related desynchronization and synchronization patterns in single-channel electrocorticogram (ECoG) obtainedAdaptive autoregressive parameters and a linear classifier were used to detect movement related desynchronization and synchronization patterns in single-channel electrocorticogram (ECoG) obtained from implanted electrode grids. The best classification accuracies found had more than 90% hits and less than 10% false positives. The findings show that the detection of event-related desynchronization and synchronization in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.

• Lexical memory search during N400: Cortical couplings in auditory comprehension

  Authors: Gernot G Supp, Alois Schlögl, Thomas C. Gunter, M. Bernard, Gert Pfurtscheller, Hellmuth Petsche

  NeuroReport, v.15, 1209-1213 (2004).

  15