A new nonlinear similarity measure for multichannel signals

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

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

We propose a novel similarity measure, called the correntropy coefficient, sensitive to higher order moments of the signal statistics based on a similarity function called the cross-correntopy. Cross-correntropy nonlinearly maps the original time series into a high-dimensional reproducing kernel Hilbert space (RKHS). The correntropy coefficient computes the cosine of the angle between the transformed vectors. Preliminary experiments with simulated data and multichannel electroencephalogram (EEG) signals during behaviour studies elucidate the performance of the new measure versus the well-established correlation coefficient.

Original languageEnglish
Pages (from-to)222-231
Number of pages10
JournalNeural Networks
Volume21
Issue number2-3
DOIs
Publication statusPublished - Mar 2008
Externally publishedYes

Keywords

  • Biomedical application
  • EEG analysis
  • Kernel method
  • Nonlinear dependence
  • Similarly measure
  • Synchronization

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