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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-12

AUTHORS

Rafał Kuś, Piotr Tadeusz Różański, Piotr Jerzy Durka

ABSTRACT

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), 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. More... »

PAGES

94

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1475-925x-12-94

DOI

http://dx.doi.org/10.1186/1475-925x-12-94

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1026040425

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24059247


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Download the RDF metadata as:  json-ld nt turtle xml License info

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curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/1475-925x-12-94'

N-Triples is a line-based linked data format ideal for batch operations.

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Turtle is a human-readable linked data format.

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RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1475-925x-12-94'


 

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