Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog

BioMedical Engineering OnLine

Rafał Kuś¹  Piotr Tadeusz Różański¹  Piotr Jerzy Durka¹ 

[1] Faculty of Physics, University of Warsaw, ul. Hoża 69, 00-681 Warszawa, Poland

关键词: Metrics;    Gabor dictionary;    Time-frequency;    MEG;    EEG;    Matching pursuit;   

Others  :  797352
DOI  :  10.1186/1475-925X-12-94

 received in 2013-08-03, accepted in 2013-09-02,  发布年份 2013

【 摘 要 】

Background

Matching pursuit algorithm (MP), especially with recent multivariate extensions, offers unique advantages in analysis of EEG and MEG.

Methods

We propose a novel construction of an optimal Gabor dictionary, based upon the metrics introduced in this paper. We implement this construction in a freely available software for MP decomposition of multivariate time series, with a user friendly interface via the Svarog package (Signal Viewer, Analyzer and Recorder On GPL, http://braintech.pl/svarog webcite), and provide a hands-on introduction to its application to EEG. Finally, we describe numerical and mathematical optimizations used in this implementation.

Results

Optimal Gabor dictionaries, based on the metric introduced in this paper, for the first time allowed for a priori assessment of maximum one-step error of the MP algorithm. Variants of multivariate MP, implemented in the accompanying software, are organized according to the mathematical properties of the algorithms, relevant in the light of EEG/MEG analysis. Some of these variants have been successfully applied to both multichannel and multitrial EEG and MEG in previous studies, improving preprocessing for EEG/MEG inverse solutions and parameterization of evoked potentials in single trials; we mention also ongoing work and possible novel applications.

Conclusions

Mathematical results presented in this paper improve our understanding of the basics of the MP algorithm. Simple introduction of its properties and advantages, together with the accompanying stable and user-friendly Open Source software package, pave the way for a widespread and reproducible analysis of multivariate EEG and MEG time series and novel applications, while retaining a high degree of compatibility with the traditional, visual analysis of EEG.

【 授权许可】

   
2013 Kuś et al.; licensee BioMed Central Ltd.

【 图 表 】

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