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

作者信息

Wang Xue, Chen Yonghong, Ding Mingzhou

机构信息

J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1111 SW 16th Avenue, Gainesville. FL 32611, USA.

出版信息

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

DOI:10.1109/TBME.2007.895751
PMID:18018692
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.

摘要

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