A. Lueschow

Publications of A. Lueschow

• Nonlinear Time Series Analysis of Human Alpha Rhythm

  Authors: G. Nolte, T Sander, A. Lueschow, B. A. Pearlmutter

  06/2002;

  Nonlinearity is often deduced by showing that a dataset signicantly deviates from its phase randomized versions, i.e. surrogate data. For real data, however, non-stationarities like artifacts andNonlinearity is often deduced by showing that a dataset signicantly deviates from its phase randomized versions, i.e. surrogate data. For real data, however, non-stationarities like artifacts and onsets and offsets of rhythmic activity will cause false positives. We propose a new test which detects dynamical nonlinearity by measuring time-asymmetry, using surrogate data merely to estimate the standard deviation of the process.

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• Cardiac artifact subspace identification and elimination in cognitive MEG data using time-delayed decorrelation

  Authors: T.H. Sander, G. Wubbeler, A. Lueschow, G. Curio, L. Trahms

  Biomedical Engineering, IEEE Transactions on. 05/2002;

  To reduce physiological artifacts in magnetoencephalographic (MEG) and electroencephalographic recordings, a number of methods have been applied in the past such as principal component analysis,To reduce physiological artifacts in magnetoencephalographic (MEG) and electroencephalographic recordings, a number of methods have been applied in the past such as principal component analysis, signal-space projection, regression using secondary information, and independent component analysis. This method has become popular as it does not have constraints such as orthogonality between artifact and signal or the need for a priori information. Applying the time-delayed decorrelation algorithm to raw data from a visual stimulation MEG experiment, we show that several of the independent components can be attributed to the cardiac artifact. Calculating an average cardiac activity shows that physiologically different excitation states of the heart produce similar field distributions in the MEG sensor system. This is equivalent to differing spectral properties of cardiac field distributions in the raw data. As a consequence, the algorithm combines, e.g., the R peak and the T wave of the cardiac cycle into a single component and the one-to-one assignment of each independent component with a physiological source is not justified in this case. To improve the signal quality of visually evoked fields, the multidimensional cardiac artifact subspace is suppressed from the data. To assess the preservation of the evoked signal after artifact suppression, a geometrical and a temporal measure are introduced. The suppression of cardiac and a wave artifacts allows, in our experimental setting, the reduction of the number of epochs to one half while preserving the visually evoked signal.

• MEG-analysis using the Hilbert transform.

  Authors: A Link, C Elster, T Sander, A. Lueschow, G Curio, L Trahms

  Biomedizinische Technik. Biomedical engineering. 02/2002; 47 Suppl 1 Pt 2:577-80.

  Event-related fields (ERFs) measured by magnetoencephalography (MEG) upon visual stimulation are analysed by Hilbert transformation. The Hilbert transform of real-valued measured ERF is an analyticEvent-related fields (ERFs) measured by magnetoencephalography (MEG) upon visual stimulation are analysed by Hilbert transformation. The Hilbert transform of real-valued measured ERF is an analytic complex signal, represented by phase and amplitude. The temporal behaviour of the derivative of the phase, i.e. the instantaneous frequency, allows to distinguish time intervals containing meaningful signal from noise. On the basis of both phase and amplitude, energies and latencies of single event ERFs can be determined.

• Time-delayed decorrelation for the identification of cardiac artifact components in MEG data.

  Authors: T H Sander, A. Lueschow, G Curio, L Trahms

  Biomedizinische Technik. Biomedical engineering. 02/2002; 47 Suppl 1 Pt 2:573-6.

  The time-delayed decorrelation (TDD) Independent Component Analysis (ICA) applied to continuous multichannel magnetoencephalographic (MEG) recordings is a straightforward calculation in the timeThe time-delayed decorrelation (TDD) Independent Component Analysis (ICA) applied to continuous multichannel magnetoencephalographic (MEG) recordings is a straightforward calculation in the time domain: Its main ingredient is a calculation of the correlation between signals having different time shifts with respect to each other. The equivalent mathematical representation in the frequency domain and a simple simulation show that TDD exploits different complex frequency spectra of the sources. Applying ICA to 80-channel whole head MEG data obtained under visual stimulation we can assign five of the 80 source components to the cardiac artifact. Their complex spectra exhibit a randomly distributed phase showing that TDD is capable of isolating quasi-periodic signals even for the case of an unspecified phase.

• Identification Of Visually Evoked Brain Activity And Cardiac Artifact Components Through Time-Delayed Decorrelation

  Authors: G. W Ubbeler, L Trahms, Physikalisch-technische Bundesanstalt, A. Lueschow, G Curio, Universitatsklinikum Benjamin Franklin

  01/2002;

  Applying the time-delayed decorrelation (TDD) algorithm to raw data from a visual stimulation magnetoencephalographic (MEG) experiment we investigate the viability of classification of componentsApplying the time-delayed decorrelation (TDD) algorithm to raw data from a visual stimulation magnetoencephalographic (MEG) experiment we investigate the viability of classification of components into artifact or stimulus related components. The TDD components associated with the cardiac artifact show a striking similarity with a Principal Component Analysis (PCA) of the averaged cardiac artifact. This could be due to a violation of the TDD assumptions by the cardiac artifact. Two TDD components have time series peaking consistently at the time expected for the primary response due to the visual stimulation, but their field maps are different from the earliest signal in the average response. An identification of TDD components with physiological sources needs further investigation.

• MEG/EEG sources of the 170 ms response to faces are co-localized in the fusiform gyrus

  Authors: I. Deffke, T. Sander, J. Heidenreich, W. Sommer, G. Curio, A. Lueschow

  Neuroimage. 35:1495-1501.

  The 170 ms electrophysiological processing stage (N170 in EEG, M170 in MEG) is considered an important computational step in face processing. Hence its neuronal sources have been modelled in severalThe 170 ms electrophysiological processing stage (N170 in EEG, M170 in MEG) is considered an important computational step in face processing. Hence its neuronal sources have been modelled in several studies. The current study aimed to specify the relation of the dipolar sources underlying N170 and M170. Whole head EEG and MEG were measured simultaneously during the presentation of unfamiliar faces. An Independent Component Analysis (ICA) was applied to the data prior to localization. N170 and M170 were then modelled with a pair of dipoles in a four-shell ellipse (EEG) / homogeneous sphere (MEG) arranged symmetrically across midline. The dipole locations were projected onto the individual structural MR brain images. Dipoles were localized in fusiform gyri in ten out of eleven individuals for EEG and in seven out of eleven for MEG. N170 and M170 were colocalized in the fusiform gyrus in six individuals. The ICA shifted some of the single subject dipoles up from cerebellum to fusiform gyrus mainly due to the removal of cardiac activity. The group mean dipole locations were also found in posterior fusiform gyri, and did not differ significantly between EEG and MEG. The result was replicated in a repeated measurement three months later.