A new nonlinear similarity measure for multichannel signals

DOI10.1016/j.neunet.2007.12.039zbMath1254.94019OpenAlexW2011292340WikidataQ47914971 ScholiaQ47914971MaRDI QIDQ1932002

Jian-Wu Xu, Hovagim Bakardjian, Jose C. Principe, Andrzej Cichocki

Publication date: 17 January 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2007.12.039

zbMATH Keywords

synchronizationkernel methodnonlinear dependenceEEG analysisbiomedical applicationsimilarly measure

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Measures of information, entropy (94A17)

Related Items (4)

A test of independence based on a generalized correlation function ⋮ Regularized correntropy criterion based semi-supervised ELM ⋮ A new nonlinear similarity measure for multichannel signals ⋮ On the characterization of cognitive tasks using activity-specific short-lived synchronization between electroencephalography channels

Cites Work

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• Surrogate time series.
• A new nonlinear similarity measure for multichannel signals
• Measuring statistical dependences in a time series
• Generalized correlation function: definition, properties, and application to blind equalization
• Theory of Reproducing Kernels
• A robust method for detecting interdependences: Application to intracranially recorded EEG
• Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets

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