Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal

Listed in Datasets publication by group Laboratory of Integrated Brain Imaging

By Haiguang Wen1, Zhongming Liu2

1. Purdue University 2. Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, Purdue University

Matlab toolbox for separating the fractal (scale-free) component and oscillatory component in the power spectrum from the mixed time series

Version 1.0 - published on 30 Sep 2015 doi:10.4231/R7WQ01R7 - cite this Archived on 25 Oct 2016

Licensed under GNU General Public License 3.0

Description

Neurophysiological field-potential signals con- sist of both arrhythmic and rhythmic patterns indicative of the fractal and oscillatory dynamics arising from likely distinct mechanisms. Here, we present a new method, namely the irregular-resampling auto-spectral analysis (IRASA), to separate fractal and oscillatory components in the power spectrum of neurophysiological signal according to their distinct temporal and spectral characteristics. In this method, we irregularly resampled the neural signal by a set of non-integer factors, and statistically summarized the auto-power spectra of the resampled signals to separate the fractal component from the oscillatory component in the frequency domain. We tested this method on simulated data and demonstrated that IRASA could robustly separate the fractal component from the oscillatory component. In addition, applications of IRASA to macaque electrocor- ticography and human magnetoencephalography data revealed a greater power-law exponent of fractal dynamics during sleep compared to wakefulness. The temporal fluctuation in the broadband power of the fractal compo- nent revealed characteristic dynamics within and across the eyes-closed, eyes-open and sleep states. These results demonstrate the efficacy and potential applications of this method in analyzing electrophysiological signatures of large-scale neural circuit activity. We expect that the pro- posed method or its future variations would potentially allow for more specific characterization of the differential contributions of oscillatory and fractal dynamics to dis- tributed neural processes underlying various brain functions. 

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Matlab toolbox for separating fractal (scale-free) component and oscillatory component in the power spectrum from mixed time series

Version 1.0

Reference:
Wen H. and Liu Z. (2015) Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signals

File list:
Example.m - Give an example for synthesis data, and an example for ECoG data
Four function files: amri_sig_fractal(), amri_sig_genfrac(), amri_sig_plawfit(), amri_sig_filtfft().
ECoG_data.mat: a vector (5-min time series of sampling rate 1kHz recorded from one sensor in the occipital area of macaque brain).

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