title: Testing for statistical significance in bispectra: a surrogate data approach and application to neuroscience.
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.
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1111 SW 16th Avenue, Gainesville. FL 32611, USA. xwang@bme.ufl.edu
authors.forename: Xue
authors.initials: X
authors.lastname: Wang
authors.forename: Yonghong
authors.initials: Y
authors.lastname: Chen
authors.forename: Mingzhou
authors.initials: M
authors.lastname: Ding
mesh_terms: D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D000882:Haplorhini; D009044:Motor Cortex / Q000502:physiology*; D009488:Neurosciences / Q000379:methods*