Nicole Krämer

Publications of Nicole Krämer

• Comparison of Granger Causality and Phase Slope Index.

  Authors: Guido Nolte, Andreas Ziehe, Nicole Krämer, Florin Popescu, Klaus-Robert Müller

  Journal of Machine Learning Research - Proceedings Track. 01/2010; 6:267-276.

• 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.

• 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

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