ArticleBiomedical engineering online2013
Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog.
Article in Biomedical engineering online, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- CB-1 receptor agonist drastically changes oscillatory activity, defining active sleep.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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- Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics.bioRxiv : the preprint server for biology · 2024Article
- ERP Analysis Using a Multi-Channel Matching Pursuit Algorithm.Neuroinformatics · 2022Article
- Trends in Compressive Sensing for EEG Signal Processing Applications.Sensors (Basel, Switzerland) · 2020Review
- EEG Transients in the Sigma Range During non-REM Sleep Predict Learning in Dogs.Scientific reports · 2017Article
- Spindles in Svarog: framework and software for parametrization of EEG transients.Frontiers in human neuroscience · 2015Article
- Electroencephalographic profiles for differentiation of disorders of consciousness.Biomedical engineering online · 2013Article
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Abstract
backgroundMatching pursuit algorithm (MP), especially with recent multivariate extensions, offers unique advantages in analysis of EEG and MEG.
methodsWe 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.
resultsOptimal 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.
conclusionsMathematical 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.
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