Testing for statistical significance in bispectra: A surrogate data approach and application to neuroscience

Xue Wang*, Yonghong Chen, Mingzhou Ding

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Interactions among neural signals in different frequency bands have become a focus of strong interest in neuroscience. Bispectral analysis, a type of higher order spectral analysis, provides us with the ability to investigate such nonlinear interactions. Based on the fact that the bispectrum of a linear Gaussian process is zero, a surrogate data method was proposed to test the null hypothesis that the original data were generated by a linear Gaussian process. The method was first tested on two simulation examples. It was then applied to local field potential recordings from a monkey performing a visuomotor task. The analysis reveals nonzero bispectra for beta and gamma band activities in the premotor cortex. The rigorous statistical framework proves essential in establishing these results.

Original languageEnglish (US)
Pages (from-to)1974-1982
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume54
Issue number11
DOIs
StatePublished - Nov 2007

Keywords

  • Bispectrum
  • Local field potential
  • Quadratic phase coupling (QPC)
  • Surrogate data

ASJC Scopus subject areas

  • Biomedical Engineering

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