Analysis of EEG transients by means of matching pursuit

Durka, P.J.; Blinowska, Katarzyna J.

doi:10.1007/bf02584459

Cited by 94 publications

(43 citation statements)

References 6 publications

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“…To show the usefulness of the TCT for the investigation of the frequency extent of the signal, we employed a wavelet decomposition of the same data segments using complex Gabor functions (Durka and Blinowska 1995;Koenig et al 2001), and applying the TCT to each timefrequency point. The wavelets covered a frequency range between 0.5 and 25 Hz.…”

Section: Simulations and Examplesmentioning

confidence: 99%

A Method to Determine the Presence of Averaged Event-Related Fields Using Randomization Tests

2010

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We present a simple and effective method to test whether an event consistently activates a set of brain electric sources across repeated measurements of event-related scalp field data. These repeated measurements can be single trials, single subject ERPs, or ERPs from different studies. The method considers all sensors simultaneously, but can be applied separately to each time frame or frequency band of the data. This allows limiting the analysis to time periods and frequency bands where there is positive evidence of a consistent relation between the event and some brain electric sources. The test may therefore avoid false conclusions about the data resulting from an inadequate selection of the analysis window and bandpass filter, and permit the exploration of alternate hypotheses when group/condition differences are observed in evoked field data. The test will be called topographic consistency test (TCT). The statistical inference is based on simple randomization techniques. Apart form the methodological introduction, the paper contains a series of simulations testing the statistical power of the method as function of number of sensors and observations, a sample analysis of EEG potentials related to self-initiated finger movements, and Matlab source code to facilitate the implementation. Furthermore a series of measures to control for multiple testing are introduced and applied to the sample data.

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Section: Simulations and Examplesmentioning

confidence: 99%

A Method to Determine the Presence of Averaged Event-Related Fields Using Randomization Tests

2010

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“…Vialatte [36] applied to SSVEP so-called "bump" analysis based on complex Morlet wavelets followed by construction of "bumps" based on half ellipsoid basis functions. However, these methods suffered from the limitation of t-f resolution inherent in the wavelet method [5,6]. MP outperforms other methods of t-f analysis in several aspects [1,2,3].…”

Section: Discussionmentioning

confidence: 99%

Matching Pursuit with Asymmetric Functions for Signal Decomposition and Parameterization

Blinowska¹,

Jędrzejczak²,

Kwaskiewicz³

2014

IFMBE Proceedings

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The method of adaptive approximations by Matching Pursuit makes it possible to decompose signals into basic components (called atoms). The approach relies on fitting, in an iterative way, functions from a large predefined set (called dictionary) to an analyzed signal. Usually, symmetric functions coming from the Gabor family (sine modulated Gaussian) are used. However Gabor functions may not be optimal in describing waveforms present in physiological and medical signals. Many biomedical signals contain asymmetric components, usually with a steep rise and slower decay. For the decomposition of this kind of signal we introduce a dictionary of functions of various degrees of asymmetry -from symmetric Gabor atoms to highly asymmetric waveforms. The application of this enriched dictionary to Otoacoustic Emissions and Steady-State Visually Evoked Potentials demonstrated the advantages of the proposed method. The approach provides more sparse representation, allows for correct determination of the latencies of the components and removes the "energy leakage" effect generated by symmetric waveforms that do not sufficiently match the structures of the analyzed signal. Additionally, we introduced a time-frequency-amplitude distribution that is more adequate for representation of asymmetric atoms than the conventional time-frequency-energy distribution.

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“…Furthermore, in order to obtain an optimal time and frequency resolution of these Gabor-functions, matching-pursuit has been proposed by Durka and Blinowska (1995). Matching pursuit initially introduced by Mallat and Zhang (1993) is an adaptive approximation technique that decomposes the signal into a linear expansion of waveforms, for example Gabor-functions.…”

Section: Introductionmentioning

confidence: 99%

From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition

Studer

Hoffmann

Koenig

2006

Journal of Neuroscience Methods

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Analysis of EEG transients by means of matching pursuit

Cited by 94 publications

References 6 publications

A Method to Determine the Presence of Averaged Event-Related Fields Using Randomization Tests

A Method to Determine the Presence of Averaged Event-Related Fields Using Randomization Tests

Matching Pursuit with Asymmetric Functions for Signal Decomposition and Parameterization

From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition

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