Guido Nolte

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• 36Publications

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Publications

• Cross-frequency decomposition: A novel technique for studying interactions between neuronal oscillations with different frequencies

  Authors: Vadim V Nikulin, Guido Nolte, Gabriel Curio

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 01/2012;

  OBJECTIVE: We present a novel method for the extraction of neuronal components showing cross-frequency phase synchronization. METHODS: In general the method can be applied for the detection of phaseOBJECTIVE: We present a novel method for the extraction of neuronal components showing cross-frequency phase synchronization. METHODS: In general the method can be applied for the detection of phase interactions between components with frequencies f1 and f2, where f2≈rf1 and r is some integer. We refer to the method as cross-frequency decomposition (CFD), which consists of the following steps: (a) extraction of f1-oscillations with the spatio-spectral decomposition algorithm (SSD); (b) frequency modification of the f1-oscillations obtained with SSD; and (c) finding f2-oscillations synchronous with f1-oscillations using least-squares estimation. RESULTS: Our simulations showed that CFD was capable of recovering interacting components even when the signal-to-noise ratio was as low as 0.01. An application of CFD to the real EEG data demonstrated that cross-frequency phase synchronization between alpha and beta oscillations can originate from the same or remote neuronal populations. CONCLUSIONS: CFD allows a compact representation of the sets of interacting components. The application of CFD to EEG data allows differentiating cross-frequency synchronization arising due to genuine neurophysiological interactions from interactions occurring due to quasi-sinusoidal waveform of neuronal oscillations. SIGNIFICANCE: CFD is a method capable of extracting cross-frequency coupled neuronal oscillations even in the presence of strong noise.

• Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space

  Authors: Arne Ewald, Laura Marzetti, Filippo Zappasodi, Frank C Meinecke, Guido Nolte

  NeuroImage. 12/2011;

  The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sourcesThe imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a significant result. It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specifically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a finger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data.

• Optimal imaging of cortico-muscular coherence through a novel regression technique based on multi-channel EEG and un-rectified EMG

  Authors: Zubeyir Bayraktaroglu, Katherina von Carlowitz-Ghori, Florian Losch, Guido Nolte, Gabriel Curio, Vadim V Nikulin

  NeuroImage. 05/2011;

  Cortico-muscular coherence (CMC) reflects interactions between muscular and cortical activities as detected with EMG and EEG recordings, respectively. Most previous studies utilized EMG rectificationCortico-muscular coherence (CMC) reflects interactions between muscular and cortical activities as detected with EMG and EEG recordings, respectively. Most previous studies utilized EMG rectification for CMC calculation. Yet, recent modeling studies predicted that EMG rectification might have disadvantages for CMC evaluation. In addition, previously the effect of rectification on CMC was estimated with single-channel EEG which might be suboptimal for detection of CMC. In order to optimally detect CMC with un-rectified EMG and resolve the issue of EMG rectification for CMC estimation, we introduce a novel method, Regression CMC (R-CMC), which maximizes the coherence between EEG and EMG. The core idea is to use multiple regression where narrowly filtered EEG signals serve as predictors and EMG is the dependent variable. We investigated CMC during isometric contraction of the abductor pollicis brevis muscle. In order to facilitate the comparison with previous studies, we estimated the effect of rectification with frequently used Laplacian filtering and C3/C4 vs. linked earlobes. For all three types of analysis, we detected CMC in the beta frequency range above the contralateral sensorimotor areas. The R-CMC approach was validated with simulations and real data and was found capable of recovering CMC even in case of high levels of background noise. When using single channel data, there were no changes in the strength of CMC estimated with rectified or un-rectified EMG - in agreement with the previous findings. Critically, for both Laplacian and R-CMC analyses EMG rectification resulted in significantly smaller CMC values compared to un-rectified EMG. Thus, the present results provide empirical evidence for the predictions from the earlier modeling studies that rectification of EMG can reduce CMC.

• A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition

  Authors: Vadim V Nikulin, Guido Nolte, Gabriel Curio

  NeuroImage. 01/2011;

  Neuronal oscillations have been shown to underlie various cognitive, perceptual and motor functions in the brain. However, studying these oscillations is notoriously difficult with EEG/MEG recordingsNeuronal oscillations have been shown to underlie various cognitive, perceptual and motor functions in the brain. However, studying these oscillations is notoriously difficult with EEG/MEG recordings due to a massive overlap of activity from multiple sources and also due to the strong background noise. Here we present a novel method for the reliable and fast extraction of neuronal oscillations from multi-channel EEG/MEG/LFP recordings. The method is based on a linear decomposition of recordings: it maximizes the signal power at a peak frequency while simultaneously minimizing it at the neighboring, surrounding frequency bins. Such procedure leads to the optimization of signal-to-noise ratio and allows extraction of components with a characteristic "peaky" spectral profile, which is typical for oscillatory processes. We refer to this method as spatio-spectral decomposition (SSD). Our simulations demonstrate that the method allows extraction of oscillatory signals even with a signal-to-noise ratio as low as 1:10. The SSD also outperformed conventional approaches based on independent component analysis. Using real EEG data we also show that SSD allows extraction of neuronal oscillations (e.g., in alpha frequency range) with high signal-to-noise ratio and with the spatial patterns corresponding to central and occipito-parietal sources. Importantly, running time for SSD is only a few milliseconds, which clearly distinguishes it from other extraction techniques usually requiring minutes or even hours of computational time. Due to the high accuracy and speed, we suggest that SSD can be used as a reliable method for the extraction of neuronal oscillations from multi-channel electrophysiological recordings.

• 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. 09/2010;

  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 spatialWe 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.

• Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci-a Real World Verification for the EEG Inverse Solutions

  Authors: Piotr Zwoliński, Marcin Roszkowski, Jaroslaw Zygierewicz, Stefan Haufe, Guido Nolte, Piotr J Durka

  Neuroinformatics. 09/2010;

  This paper introduces a freely accessible database http://eeg.pl/epi , containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part ofThis paper introduces a freely accessible database http://eeg.pl/epi , containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10-20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time.

• Non-zero mean of oscillations as a mechanism for the generation of evoked responses Reply to "Amplitude asymmetry as a mechanism for the generation of slow evoked responses"

  Authors: Vadim V Nikulin, Klaus Linkenkaer-Hansen, Guido Nolte, Matias J Palva, Gabriel Curio

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 04/2010;

• Localizing and estimating causal relations of interacting brain rhythms

  Authors: Guido Nolte, Klaus-Robert Müller

  Frontiers in human neuroscience. 01/2010; 4:209.

  Estimating brain connectivity and especially causality between different brain regions from EEG or MEG is limited by the fact that the data are a largely unknown superposition of the actual brainEstimating brain connectivity and especially causality between different brain regions from EEG or MEG is limited by the fact that the data are a largely unknown superposition of the actual brain activities. Any method, which is not robust to mixing artifacts, is prone to yield false positive results. We here review a number of methods that allow for addressing this problem. They are all based on the insight that the imaginary part of the cross-spectra cannot be explained as a mixing artifact. First, a joined decomposition of these imaginary parts into pairwise activities separates subsystems containing different rhythmic activities. Second, assuming that the respective source estimates are least overlapping, yields a separation of the rhythmic interacting subsystem into the source topographies themselves. Finally, a causal relation between these sources can be estimated using the newly proposed measure Phase Slope Index (PSI). This work, for the first time, presents the above methods in combination; all illustrated using a single, simulated data set.

• 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; 1:5137-40.

  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.

• Modeling sparse connectivity between underlying brain sources for EEG/MEG

  Authors: Stefan Haufe, Ryota Tomioka, Guido Nolte, Klaus-Robert Mueller, Motoaki Kawanabe

  12/2009;

  We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conductionWe propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model, (b) the demixing is estimated jointly with the source MVAR parameters, (c) overfitting is avoided by using the Group Lasso penalty. This approach allows to extract the appropriate level cross-talk between the extracted sources and in this manner we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data, and compare to a number of existing algorithms with excellent results. Comment: 9 pages, 6 figures

• Non-zero mean and asymmetry of neuronal oscillations have different implications for evoked responses

  Authors: Vadim V Nikulin, Klaus Linkenkaer-Hansen, Guido Nolte, Gabriel Curio

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 11/2009;

  OBJECTIVE: The aim of the present study was to show analytically and with simulations that it is the non-zero mean of neuronal oscillations, and not an amplitude asymmetry of peaks and troughs, thatOBJECTIVE: The aim of the present study was to show analytically and with simulations that it is the non-zero mean of neuronal oscillations, and not an amplitude asymmetry of peaks and troughs, that is a prerequisite for the generation of evoked responses through a mechanism of amplitude modulation of oscillations. Secondly, we detail the rationale and implementation of the "baseline-shift index" (BSI) for deducing whether empirical oscillations have non-zero mean. Finally, we illustrate with empirical data why the "amplitude fluctuation asymmetry" (AFA) index should be used with caution in research aimed at explaining variability in evoked responses through a mechanism of amplitude modulation of ongoing oscillations. METHODS: An analytical approach, simulations and empirical MEG data were used to compare the specificity of BSI and AFA index to differentiate between a non-zero mean and a non-sinusoidal shape of neuronal oscillations. RESULTS: Both the BSI and the AFA index were sensitive to the presence of non-zero mean in neuronal oscillations. The AFA index, however, was also sensitive to the shape of oscillations even when they had a zero mean. CONCLUSIONS: Our findings indicate that it is the non-zero mean of neuronal oscillations, and not an amplitude asymmetry of peaks and troughs, that is a prerequisite for the generation of evoked responses through a mechanism of amplitude modulation of oscillations. SIGNIFICANCE: A clear distinction should be made between the shape and non-zero mean properties of neuronal oscillations. This is because only the latter contributes to evoked responses, whereas the former does not.

• Minimum Overlap Component Analysis (MOCA) of EEG/MEG data for more than two sources

  Authors: Guido Nolte, Laura Marzetti, Pedro Valdes Sosa

  Journal of neuroscience methods. 08/2009;

  In many situations various methods to analyze EEG/MEG data result in subspaces of the sensor space spanned by potentials of a set of sources. We propose a general model free method to decompose suchIn many situations various methods to analyze EEG/MEG data result in subspaces of the sensor space spanned by potentials of a set of sources. We propose a general model free method to decompose such a subspace into contributions from distinct sources. This unique decomposition can be achieved by first finding the respective subspace in source space using a linear inverse method and then finding the linear transformation such that the source distributions are mutually orthogonal and have a minimum overlap. The corresponding algorithm is a generalization of the recently presented 'Minimum Overlap Component Analysis' (MOCA) to more than two sources. The computational cost is negligible and the algorithm is almost never trapped in local minima. The method is illustrated with results for alpha rhythm.

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• Sparse Causal Discovery in Multivariate Time Series

  Authors: Stefan Haufe, Guido Nolte, Klaus-Robert Mueller, Nicole Kraemer

  01/2009;

  Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belonging to respectiveOur goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belonging to respective time-lagged instances. As in most cases a parsimonious causality structure is assumed, a promising approach to causal discovery consists in fitting VAR models with an additional sparsity-promoting regularization. Along this line we here propose that sparsity should be enforced for the subgroups of coefficients that belong to each pair of time series, as the absence of a causal relation requires the coefficients for all time-lags to become jointly zero. Such behavior can be achieved by means of l1-l2-norm regularized regression, for which an efficient active set solver has been proposed recently. Our method is shown to outperform standard methods in recovering simulated causality graphs. The results are on par with a second novel approach which uses multiple statistical testing. Comment: to appear in Journal of Machine Learning Research, Proceedings of the NIPS'08 workshop on Causality

• Understanding brain connectivity from EEG data by identifying systems composed of interacting sources

  Authors: Laura Marzetti, Cosimo Del Gratta, Guido Nolte

  NeuroImage. 09/2008; 42(1):87-98.

  In understanding and modeling brain functioning by EEG/MEG, it is not only important to be able to identify active areas but also to understand interference among different areas. The EEG/MEG signalsIn understanding and modeling brain functioning by EEG/MEG, it is not only important to be able to identify active areas but also to understand interference among different areas. The EEG/MEG signals result from the superimposition of underlying brain source activities volume conducted through the head. The effects of volume conduction produce spurious interactions in the measured signals. It is fundamental to separate true source interactions from noise and to unmix the contribution of different systems composed by interacting sources in order to understand interference mechanisms. As a prerequisite, we consider the problem of unmixing the contribution of uncorrelated sources to a measured field. This problem is equivalent to the problem of unmixing the contribution of different uncorrelated compound systems composed by interacting sources. To this end, we develop a principal component analysis-based method, namely, the source principal component analysis (sPCA), which exploits the underlying assumption of orthogonality for sources, estimated from linear inverse methods, for the extraction of essential features in signal space. We then consider the problem of demixing the contribution of correlated sources that comprise each of the compound systems identified by using sPCA. While the sPCA orthogonality assumption is sufficient to separate uncorrelated systems, it cannot separate the individual components within each system. To address that problem, we introduce the Minimum Overlap Component Analysis (MOCA), employing a pure spatial criterion to unmix pairs of correlates (or coherent) sources. The proposed methods are tested in simulations and applied to EEG data from human micro and alpha rhythms.

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• 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.

• Combining sparsity and rotational invariance in EEG/MEG source reconstruction

  Authors: Stefan Haufe, Vadim V Nikulin, Andreas Ziehe, Klaus-Robert Müller, Guido Nolte

  NeuroImage. 06/2008;

  We introduce Focal Vector Field Reconstruction (FVR), a novel technique for the inverse imaging of vector fields. The method was designed to simultaneously achieve two goals: a) invariance withWe introduce Focal Vector Field Reconstruction (FVR), a novel technique for the inverse imaging of vector fields. The method was designed to simultaneously achieve two goals: a) invariance with respect to the orientation of the coordinate system, and b) a preference for sparsity of the solutions and their spatial derivatives. This was achieved by defining the regulating penalty function, which renders the solutions unique, as a global l(1)-norm of local l(2)-norms. We show that the method can be successfully used for solving the EEG inverse problem. In the joint localization of 2-3 simulated dipoles, FVR always reliably recovers the true sources. The competing methods have limitations in distinguishing close sources because their estimates are either too smooth (LORETA, Minimum l(1)-norm) or too scattered (Minimum l(2)-norm). In both noiseless and noisy simulations, FVR has the smallest localization error according to the Earth Mover's Distance (EMD), which is introduced here as a meaningful measure to compare arbitrary source distributions. We also apply the method to the simultaneous localization of left and right somatosensory N20 generators from real EEG recordings. Compared to its peers FVR was the only method that delivered correct location of the source in the somatosensory area of each hemisphere in accordance with neurophysiological prior knowledge.

• Cortico-cortical networks in patients with ideomotor apraxia as revealed by EEG coherence analysis

  Authors: Lewis A Wheaton, Stephan Bohlhalter, Guido Nolte, Hiroshi Shibasaki, Noriaki Hattori, Esteban Fridman, Sherry Vorbach, Jordan Grafman, Mark Hallett

  Neuroscience letters. 04/2008; 433(2):87-92.

  We sought to determine whether coherent networks which circumvent lesioned cortex are seen in patients with ideomotor apraxia (IMA) while performing tool-use pantomimes. Five normal subjects and fiveWe sought to determine whether coherent networks which circumvent lesioned cortex are seen in patients with ideomotor apraxia (IMA) while performing tool-use pantomimes. Five normal subjects and five patients with IMA (three patients with corticobasal degeneration and two with left hemisphere stroke) underwent 64-channel EEG recording while performing three tool-use pantomimes with their left hand in a self-paced manner. Beta band (20-22 Hz) coherence indicates that normal subjects have a dominant left hemisphere network responsible for praxis preparation, which was absent in patients. Corticobasal degeneration patients showed significant coherence increase between left parietal-right premotor areas. Left hemisphere stroke patients showed significant coherence increases in a right parietofrontal network. The right hemisphere appears to store useable praxis representations in IMA patients with left hemisphere damage.

• 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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• Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources

  Authors: Cornelis J Stam, Guido Nolte, Andreas Daffertshofer

  Human brain mapping. 12/2007; 28(11):1178-93.

  OBJECTIVE: To address the problem of volume conduction and active reference electrodes in the assessment of functional connectivity, we propose a novel measure to quantify phase synchronization, theOBJECTIVE: To address the problem of volume conduction and active reference electrodes in the assessment of functional connectivity, we propose a novel measure to quantify phase synchronization, the phase lag index (PLI), and compare its performance to the well-known phase coherence (PC), and to the imaginary component of coherency (IC). METHODS: The PLI is a measure of the asymmetry of the distribution of phase differences between two signals. The performance of PLI, PC, and IC was examined in (i) a model of 64 globally coupled oscillators, (ii) an EEG with an absence seizure, (iii) an EEG data set of 15 Alzheimer patients and 13 control subjects, and (iv) two MEG data sets. RESULTS: PLI and PC were more sensitive than IC to increasing levels of true synchronization in the model. PC and IC were influenced stronger than PLI by spurious correlations because of common sources. All measures detected changes in synchronization during the absence seizure. In contrast to PC, PLI and IC were barely changed by the choice of different montages. PLI and IC were superior to PC in detecting changes in beta band connectivity in AD patients. Finally, PLI and IC revealed a different spatial pattern of functional connectivity in MEG data than PC. CONCLUSION: The PLI performed at least as well as the PC in detecting true changes in synchronization in model and real data but, at the same token and like-wise the IC, it was much less affected by the influence of common sources and active reference electrodes.

• A novel mechanism for evoked responses in the human brain

  Authors: Vadim V Nikulin, Klaus Linkenkaer-Hansen, Guido Nolte, Steven Lemm, Klaus R Müller, Risto J Ilmoniemi, Gabriel Curio

  The European journal of neuroscience. 06/2007; 25(10):3146-54.

  Magnetoencephalographic and electroencephalographic evoked responses are primary real-time objective measures of cognitive and perceptual processes in the human brain. Two mechanisms (additiveMagnetoencephalographic and electroencephalographic evoked responses are primary real-time objective measures of cognitive and perceptual processes in the human brain. Two mechanisms (additive activity and phase reset) have been debated and considered as the only possible explanations for evoked responses. Here we present theoretical and empirical evidence of a third mechanism contributing to the generation of evoked responses. Interestingly, this mechanism can be deduced entirely from the characteristics of spontaneous oscillations in the absence of stimuli. We show that the amplitude fluctuations of neuronal alpha oscillations at rest are associated with changes in the mean value of ongoing activity in magnetoencephalography, a phenomenon that we term baseline shifts associated with alpha oscillations. When stimuli modulate the amplitude of alpha oscillations, baseline shifts become the basis of a novel mechanism for the generation of evoked responses; the averaging of several trials leads to a cancellation of the oscillatory component but the baseline shift remains, which gives rise to an evoked response. We propose that the presence of baseline shifts associated with alpha oscillations can be explained by the asymmetric flow of inward and outward neuronal currents related to the generation of alpha oscillations. Our findings are relevant to the vast majority of electroencephalographic and magnetoencephalographic studies involving perceptual, cognitive and motor activity.

• Identifying interactions in mixed and noisy complex systems

  Authors: Guido Nolte, Frank C Meinecke, Andreas Ziehe, Klaus-Robert Müller

  Physical review. E, Statistical, nonlinear, and soft matter physics. 06/2006; 73(5 Pt 1):051913.

  We present a technique that identifies truly interacting subsystems of a complex system from multichannel data if the recordings are an unknown linear and instantaneous mixture of the true sources.We present a technique that identifies truly interacting subsystems of a complex system from multichannel data if the recordings are an unknown linear and instantaneous mixture of the true sources. The method is valid for arbitrary noise structure. For this, a blind source separation technique is proposed that diagonalizes antisymmetrized cross-correlation or cross-spectral matrices. The resulting decomposition finds truly interacting subsystems blindly and suppresses any spurious interaction stemming from the mixture. The usefulness of this interacting source analysis is demonstrated in simulations and for real electroencephalography data.

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• Analytic expansion of the EEG lead field for realistic volume conductors

  Authors: Guido Nolte, George Dassios

  Physics in medicine and biology. 09/2005; 50(16):3807-23.

  EEG forward calculation in realistic volume conductors using the boundary element method suffers from the fact that the solutions become inaccurate for superficial sources. Here we propose to correctEEG forward calculation in realistic volume conductors using the boundary element method suffers from the fact that the solutions become inaccurate for superficial sources. Here we propose to correct an analytical approximation of the respective lead fields with series of spherical harmonics with respect to multiple expansion points. The necessary correction depends very much on the chosen analytical approximation. We constructed the latter such that the correction can be modelled adequately within the chosen basis. Simulations for a 3-shell prolate spheroid demonstrate the accurate modelling of the lead fields. Explicit comparison with analytically known solutions was done for the 3-shell spherical volume conductor showing that relative errors are mostly far below 1% even for the most superficial sources placed directly on the innermost surface.

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• Synchronization of parietal and premotor areas during preparation and execution of praxis hand movements

  Authors: Lewis A Wheaton, Guido Nolte, Stephan Bohlhalter, Esteban Fridman, Mark Hallett

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 07/2005; 116(6):1382-90.

  OBJECTIVE: We sought to determine temporal patterns of functional connectivity between the parietal, premotor, and motor cortices during preparation and execution of praxis hand movements. METHODS:OBJECTIVE: We sought to determine temporal patterns of functional connectivity between the parietal, premotor, and motor cortices during preparation and execution of praxis hand movements. METHODS: Normal subjects were instructed to perform six transitive (tool use) and intransitive (communicative gesture) self-paced pantomimes with the right hand while recording 64-channel electroencephalography (EEG) and electromyography (EMG) from right thumb and forearm flexors. Focusing on corticocortical coherence, we explored the time-course of synchronously active parietal and premotor circuits involved in these motor tasks. Trials were marked for EMG onset and averaged across subjects to determine changes in coherence relative to baseline between parietal, premotor, and motor areas. RESULTS: Coherence of homologous electrode pairs was similar when comparing transitive and intransitive movements. During preparation, beta band (18-22 Hz) coherence was maximal between electrodes over the left parietal lobe and left premotor electrodes. Additionally during preparation, the premotor area showed high coherence to the motor hand area and the parietal cortex. Electrodes over the supplementary motor area also showed coherence to the motor and parietal, but not the premotor area. Before and during execution, a second peak of high coherence increase was present in each area that demonstrated coherence increases during preparation. There was no coherence increase between parietal and motor areas. Coherence rapidly diminished 1.5-2.0 s after movement onset. CONCLUSIONS: Patterns of increased corticocortical coupling within a parietal, premotor, and motor network are present during preparation and execution of praxis movements. SIGNIFICANCE: This study adds to evidence that parietofrontal networks may be critical for integrating preparatory and motor-related activity for praxis movements.

• Shared brain areas but not functional connections controlling movement timing and order

  Authors: Gaëtan Garraux, Christopher McKinney, Tao Wu, Kenji Kansaku, Guido Nolte, Mark Hallett

  The Journal of neuroscience : the official journal of the Society for Neuroscience. 07/2005; 25(22):5290-7.

  Virtually every aspect of the enormous repertoire of human behaviors is embedded in a sequential context, but brain mechanisms underlying the adjustment of two fundamental dimensions defining a motorVirtually every aspect of the enormous repertoire of human behaviors is embedded in a sequential context, but brain mechanisms underlying the adjustment of two fundamental dimensions defining a motor sequence (order of a series of movements and intervals separating them) as a function of a given goal are poorly understood. Using functional magnetic resonance imaging, we demonstrate that, at the neuronal level, these tasks can only be distinguished by differences in functional interactions between associative areas of common activation, which included bilateral subcortico-parieto-frontal regions, and two subcortical structures. Activity in these shared associative areas was preferentially coupled with that in right putamen during manipulation of timing and with that in right posterior cerebellum during manipulation of serial order. This finding is important because it provides evidence for an efficient organization of the brain during cognitive control of motor sequences and supports a recently proposed principle according to which the role of brain regions involved in different behavioral tasks without differential alterations in their measured activity depends on changes in their interactions with other connected areas as a function of the tasks.

• Identifying true brain interaction from EEG data using the imaginary part of coherency

  Authors: Guido Nolte, Ou Bai, Lewis Wheaton, Zoltan Mari, Sherry Vorbach, Mark Hallett

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 11/2004; 115(10):2292-307.

  OBJECTIVE: The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in manyOBJECTIVE: The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. METHODS: We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. RESULTS: We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2-4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. CONCLUSIONS: It is possible to reliably detect brain interaction during movement from EEG data. SIGNIFICANCE: The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.

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• Journal of Machine Learning Research 5 (2004) 777--800 Submitted 12/03; Revised 5/04; Published 7/04 A Fast Algorithm for Joint Diagonalization with Non-orthogonal

  Authors: Andreas Ziehe, Pavel Laskov, Kekul Estrasse, Guido Nolte

  10/2004;

  A new efficient algorithm is presented for joint diagonalization of several matrices. The algorithm is based on the Frobenius-norm formulation of the joint diagonalization problem, and addressesA new efficient algorithm is presented for joint diagonalization of several matrices. The algorithm is based on the Frobenius-norm formulation of the joint diagonalization problem, and addresses diagonalization with a general, non-orthogonal transformation. The iterative scheme of the algorithm is based on a multiplicative update which ensures the invertibility of the diagonalizer. The algorithm 's efficiency stems from the special approximation of the cost function resulting in a sparse, block-diagonal Hessian to be used in the computation of the quasi-Newton update step. Extensive numerical simulations illustrate the performance of the algorithm and provide a comparison to other leading diagonalization methods. The results of such comparison demonstrate that the proposed algorithm is a viable alternative to existing state-of-the-art joint diagonalization algorithms.

• Looking for faces: Attention modulates early occipitotemporal object processing

  Authors: Andreas Lueschow, Tilmann Sander, Stephan G Boehm, Guido Nolte, Lutz Trahms, Gabriel Curio

  Psychophysiology. 06/2004; 41(3):350-60.

  Looking for somebody's face in a crowd is one of the most important examples of visual search. For this goal, attention has to be directed to a well-defined perceptual category. When thisLooking for somebody's face in a crowd is one of the most important examples of visual search. For this goal, attention has to be directed to a well-defined perceptual category. When this categorically selective process starts is, however, still unknown. To this end, we used magnetoencephalography (MEG) recorded over right human occipitotemporal cortex to investigate the time course of attentional modulation of perceptual processes elicited by faces and by houses. The first face-distinctive MEG response was observed at 160-170 ms (M170). Nevertheless, attention did not start to modulate face processing before 190 ms. The first house-distinctive MEG activity was also found around 160-170 ms. However, house processing was not modulated by attention before 280 ms (90 ms later than face processing). Further analysis revealed that the attentional modulation of face processing is not due to later, for example, back-propagated activation of the M170 generator. Rather, subsequent stages of occipitotemporal object processing were modulated in a category-specific manner and with preferential access to face processing.

• MEG Source Localization Using an MLP With a

  Authors: Sung Chan Jun, Barak A. Pearlmutter, Guido Nolte

  07/2003;

  We present a system that takes realistic magnetoencephalographic (MEG) signals and localizes a single dipole to reasonable accuracy in real time. At its heart is a multilayer perceptron (MLP) whichWe present a system that takes realistic magnetoencephalographic (MEG) signals and localizes a single dipole to reasonable accuracy in real time. At its heart is a multilayer perceptron (MLP) which takes the sensor measurements as inputs, uses one hidden layer, and generates as outputs the amplitudes of receptive fields holding a distributed representation of the dipole location. We trained this Soft-MLP on dipolar sources with real brain noise and converted the network's output into an explicit Cartesian coordinate representation of the dipole location using two different decoding strategies. The proposed Soft-MLPs are much more accurate than previous networks which output source locations in Cartesian coordinates. Hybrid Soft-MLP-start-LM systems, in which the Soft-MLP output initializes Levenberg--Marquardt, retained their accuracy of 0.28 cm with a decrease in computation time from 36 ms to 30 ms. We apply the Soft-MLP localizer to real MEG data separated by a blind source separation algorithm, and compare the Soft-MLP dipole locations to those of a conventional system.

• MEG source localization using an MLP with a distributed output representation

  Authors: Sung Chan Jun, Barak A. Pearlmutter, Guido Nolte

  IEEE transactions on bio-medical engineering. 07/2003; 50(6):786-9.

  We present a system that takes realistic magnetoencephalographic (MEG) signals and localizes a single dipole to reasonable accuracy in real time. At its heart is a multilayer perceptron (MLP) whichWe present a system that takes realistic magnetoencephalographic (MEG) signals and localizes a single dipole to reasonable accuracy in real time. At its heart is a multilayer perceptron (MLP) which takes the sensor measurements as inputs, uses one hidden layer, and generates as outputs the amplitudes of receptive fields holding a distributed representation of the dipole location. We trained this Soft-MLP on dipolar sources with real brain noise and converted the network's output into an explicit Cartesian coordinate representation of the dipole location using two different decoding strategies. The proposed Soft-MLPs are much more accurate than previous networks which output source locations in Cartesian coordinates. Hybrid Soft-MLP-start-LM systems, in which the Soft-MLP output initializes Levenberg-Marquardt, retained their accuracy of 0.28 cm with a decrease in computation time from 36 ms to 30 ms. We apply the Soft-MLP localizer to real MEG data separated by a blind source separation algorithm, and compare the Soft-MLP dipole locations to those of a conventional system.

• Fast Robust Subject-Independent Magnetoencephalographic Source Localization using an Artificial Neural Network

  Authors: Barak A. Pearlmutter, Guido Nolte

  11/2002;

  We describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained toWe describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signals and head position to dipole location. Including head position overcomes the previous need to retrain for each subject and session. The training dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.3 ms with an average error of 1.04 cm. A few iterations of a Levenberg-Marquardt routine using the MLP's output as its initial guess took 47 ms and improved the accuracy to 0.54 cm. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard commercial software.

• Fast accurate MEG source localization using a multilayer

  Authors: Sung Chan Jun, Barak A. Pearlmutter, Guido Nolte

  08/2002;

  Iterative gradient methods like Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately LMIterative gradient methods like Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a multilayer perceptron (MLP) as a real-time localizer. We used an analytical model of quasistatic electromagnetic propagation through a spherical head to map randomly chosen dipoles to sensor activities according to the sensor geometry of a 4D Neuroimaging Neuromag-122 MEG system, and trained a MLP to invert this mapping in the absence of noise or in the presence of various sorts of noise such as white Gaussian noise, correlated noise, or real brain noise. A MLP structure was chosen to trade off computation and accuracy. This MLP was trained four times, with each type of noise. We measured the effects of initial guesses on LM performance, which motivated a hybrid MLPstart -LM method, in which the trained MLP initializes LM. We also compared the localization performance of LM, MLPs, and hybrid MLP-start-LMs for realistic brain signals. Trained MLPs are much faster than other methods, while the hybrid MLP-start-LMs are faster and more accurate than fixed-4-start-LM. In particular, the hybrid MLP-start-LM initialized by a MLP trained with the real brain noise dataset is 60 times faster and is comparable in accuracy to random-20-start-LM, and this hybrid system (localization error: 0.28 cm, computation time: 36 ms) shows almost as good performance as optimal-1-start-LM (localization error: 0.23 cm, computation time: 22 ms), which initializes LM with the correct dipole location. MLPs trai...

• Fast accurate MEG source localization using a multilayer perceptron trained with real brain noise

  Authors: Sung Chan Jun, Barak A. Pearlmutter, Guido Nolte

  Physics in medicine and biology. 08/2002; 47(14):2547-60.

  Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately,Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately, LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a multilayer perceptron (MLP) as a real-time localizer. We used an analytical model of quasistatic electromagnetic propagation through a spherical head to map randomly chosen dipoles to sensor activities according to the sensor geometry of a 4D Neuroimaging Neuromag-122 MEG system, and trained a MLP to invert this mapping in the absence of noise or in the presence of various sorts of noise such as white Gaussian noise, correlated noise, or real brain noise. A MLP structure was chosen to trade off computation and accuracy. This MLP was trained four times, with each type of noise. We measured the effects of initial guesses on LM performance, which motivated a hybrid MLP-start-LM method, in which the trained MLP initializes LM. We also compared the localization performance of LM, MLPs, and hybrid MLP-start-LMs for realistic brain signals. Trained MLPs are much faster than other methods, while the hybrid MLP-start-LMs are faster and more accurate than fixed-4-start-LM. In particular, the hybrid MLP-start-LM initialized by a MLP trained with the real brain noise dataset is 60 times faster and is comparable in accuracy to random-20-start-LM, and this hybrid system (localization error: 0.28 cm, computation time: 36 ms) shows almost as good performance as optimal-1-start-LM (localization error: 0.23 cm, computation time: 22 ms), which initializes LM with the correct dipole location. MLPs trained with noise perform better than the MLP trained without noise, and the MLP trained with real brain noise is almost as good an initial guesser for LM as the correct dipole location.

• Fast robust MEG source localization using MLPs

  Authors: Sung Chan Jun, Barak A. Pearlmutter, Guido Nolte

  06/2002;

  Source localization from MEG data in real time requires algorithms which are robust, fully automatic, and very fast. We present two neural network systems which are able to localize a single dipoleSource localization from MEG data in real time requires algorithms which are robust, fully automatic, and very fast. We present two neural network systems which are able to localize a single dipole to reasonable accuracy within a fraction of a millisecond, even when the signals are contaminated by considerable noise. The first network is a multilayer perceptron (MLP) which takes the sensor measurements as inputs, uses two hidden layers, and outputs source location in Cartesian coordinates. After training with random dipolar sources contaminated by real noise, localization of a single dipole could be performed within 300 microseconds on an 800 Mhz Athlon workstation, with an average localization error of 1.15 cm. To improve the accuracy to 0.28 cm, one can apply a few iterations of conventional Levenberg-Marquardt (LM) minimization using the MLP output as the initial guess. The combined method is about twenty times faster than multistart LM localization with comparable accuracy. In a second network with only one hidden layer, the outputs were the amplitudes of 193 evenly distributed Gaussian functions holding a soft distributed representation of the dipole location. We trained this network on dipolar sources with real noise, and externally converted the network's output into an explicit Cartesian coordinate representation of the dipole location. This new network had an improved localization accuracy of 0.87 cm, while localization time was lengthened to about 800 microseconds.

• Reply to Chéron and Dan

  Authors: Fabian Klostermann, Guido Nolte, Gabriel Curio

  Neuroreport. 07/2001; 12(8):A52.

• Independent Component Analysis of Non-invasively Recorded Cortical Magnetic DC-fields in Humans

  Authors: Andreas Ziehe, Guido Nolte, Bruno-Marcel Mackert, Gabriel Curio, Gmd Forschungszentrum, Informationstechnik Gmbh, Schlo Birlinghoven

  01/1999;

  Artifacts in magnetoneurography (MNG) data due to endogenous biological noise sources, e.g. heart signal, can be four orders of magnitude higher than the signal of interest. Therefore it is importantArtifacts in magnetoneurography (MNG) data due to endogenous biological noise sources, e.g. heart signal, can be four orders of magnitude higher than the signal of interest. Therefore it is important to establish effective artifact reduction methods. We propose a blind source separation algorithm using only second order temporal correlations for cleaning bio-magnetic measurements of evoked responses in the peripheral nervous system. The algorithm showed its efficiency by eliminating disturbances originating from biological and technical noise sources and successfully extracting the signal of interest. This yields a significant improvement of the neuro-magnetic source analysis. Keywords Biomedical data processing, Artifact reduction, Biomagnetism, Magnetoneurography, Blind source separation, Independent component analysis. Acknowledgment We acknowledge valuable discussions with members of the Biophysics Group at PhysikalischTechnische Bundesanstalt (PTB) Berlin. All MNG studies were p...

• Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions

  Authors: Piotr Zwoliński, Marcin Roszkowski, Jaroslaw Żygierewicz, Stefan Haufe, Guido Nolte, Piotr J Durka

  This paper introduces a freely accessible database http://eeg.pl/epi, containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part ofThis paper introduces a freely accessible database http://eeg.pl/epi, containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time.