Guido Nolte
Publications
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3.12Impact points
Cross-frequency decomposition: A novel technique for studying interactions between neuronal oscillations with different frequencies.
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 phase interactions between components with frequencies f1 and f2, where f2≈rf1 and r is some integer. We re... [more] 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 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.
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Alleviating the Influence of Weak Data Asymmetries on Granger-Causal Analyses.
Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Tel Aviv, Israel, March 12-15, 2012. Proceedings; 01/2012
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5.74Impact points
Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space.
NeuroImage. 12/2011; 60(1):476-88.
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 sources cannot generate a significant result. It does not mean, however, that volume conduction is irrelevan... [more] 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 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.
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4.63Impact points
Music algorithm to localize sources with unknown directivity in acoustic imaging
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on; 06/2011
The Multiple Signal Classification (MUSIC) algorithm for acoustic imaging most commonly assumes that all sources have undirectional radiation pattern. We here propose a modification of this algorithm such that the concept is applicable for arbitrary directional characteristics of the sources. This i... [more] The Multiple Signal Classification (MUSIC) algorithm for acoustic imaging most commonly assumes that all sources have undirectional radiation pattern. We here propose a modification of this algorithm such that the concept is applicable for arbitrary directional characteristics of the sources. This is accomplished by fitting for each frequency the real valued amplitudes of the acoustic model rather than assuming a fixed functional form. The mathematical problem can be solved analytically resulting in an eigenvalue problem of a real valued Hamiltonian matrix. The performance is illustrated in simulations using pure monopolar, dipolar and quadrupolar sources.
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5.74Impact points
Optimal imaging of cortico-muscular coherence through a novel regression technique based on multi-channel EEG and un-rectified EMG.
NeuroImage. 05/2011; 57(3):1059-67.
Cortico-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 disadv... [more] Cortico-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.
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5.74Impact points
Large-scale EEG/MEG source localization with spatial flexibility.
NeuroImage. 01/2011; 54(2):851-9.
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 spatial basis fields. It is designed to recover multiple sources of possibly different extent and depth, while being... [more] 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 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.
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Music algorithm to localize sources with unknown directivity in acoustic imaging.
Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic; 01/2011
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5.74Impact points
A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition.
NeuroImage. 01/2011; 55(4):1528-35.
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 recordings due to a massive overlap of activity from multiple sources and also due to the strong background noi... [more] 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 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.
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3.05Impact points
Open database of epileptic EEG with MRI and postoperational assessment of foci--a real world verification for the EEG inverse solutions.
Neuroinformatics. 12/2010; 8(4):285-99.
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 of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical elec... [more] 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 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.
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Localization of class-related mu-rhythm desynchronization in motor imagery based Brain-Computer Interface sessions
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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. Our approach is based on localization of single-trial Fourier coefficients using sparse basis field expansions (... [more] 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. 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.
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2.15Impact points
Modeling Sparse Connectivity Between Underlying Brain Sources for EEG/MEG
Biomedical Engineering, IEEE Transactions on. 09/2010;
We propose a novel technique to assess functional brain connectivity in electroencephalographic (EEG)/magnetoencephalographic (MEG) signals. Our method, called sparsely connected sources analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the foll... [more] We propose a novel technique to assess functional brain connectivity in electroencephalographic (EEG)/magnetoencephalographic (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: 1) the EEG/MEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model; 2) the demixing is estimated jointly with the source MVAR parameters; and 3) overfitting is avoided by using the group lasso penalty. This approach allows us to extract the appropriate level of crosstalk 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 it to a number of existing algorithms with excellent results.
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3.12Impact points
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"
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 04/2010;
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Comparison of Granger Causality and Phase Slope Index.
Journal of Machine Learning Research - Proceedings Track. 01/2010; 6:267-276.
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1.55Impact points
Localizing and estimating causal relations of interacting brain rhythms.
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 brain activities. Any method, which is not robust to mixing artifacts, is prone to yield false positive result... [more] 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 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.
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Localization of class-related mu-rhythm desynchronization in motor imagery based brain-computer interface sessions.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 01/2010; 2010:5137-40.
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. Our approach is based on localization of single-trial Fourier coefficients using sparse basis field expansions (... [more] 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. 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.
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Modeling sparse connectivity between underlying brain sources for EEG/MEG
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 conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a li... [more] 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 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
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3.12Impact points
Non-zero mean and asymmetry of neuronal oscillations have different implications for evoked responses.
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, that is a prerequisite for the generation of evoked responses through a mechanism of amplitude modulation ... [more] 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, 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.
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2.30Impact points
Minimum Overlap Component Analysis (MOCA) of EEG/MEG data for more than two sources.
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 such a subspace into contributions from distinct sources. This unique decomposition can be achieved by fir... [more] 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 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
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 respective time-lagged instances. As in most cases a parsimonious causality structure is assumed, a promising appro... [more] 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 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
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5.74Impact points
Understanding brain connectivity from EEG data by identifying systems composed of interacting sources.
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 signals result from the superimposition of underlying brain source activities volume conducted through the h... [more] 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 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.
Following (21)
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George Dassios
University of Patras -
Jutta Kunz
Carl von Ossietzky Universität Oldenburg -
Risto J Ilmoniemi
Aalto University -
Zoltan Mari
Johns Hopkins University -
Mauro Gianni Perrucci
Università degli Studi G. d'Annunzio Chieti e Pescara
