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

IEEE Trans Biomed Eng. 2007 Nov;54(11):1974-82. doi: 10.1109/TBME.2007.895751.

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Animals
  • Data Interpretation, Statistical*
  • Electroencephalography / methods*
  • Evoked Potentials, Motor / physiology*
  • Haplorhini
  • Motor Cortex / physiology*
  • Neurosciences / methods*