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Updated: May 7, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Filter-based multiscale entropy analysis of complex physiological time series.

Yuesheng Xu¹, Liang Zhao

¹Department of Mathematics, Syracuse University, Syracuse, New York 13244, USA and Guangdong Province Key Lab of Computational Science, Sun Yat-sen University, Guangzhou 510275, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|September 17, 2013

Summary

This summary is machine-generated.

Filter-based multiscale entropy (FME) offers a flexible approach to analyzing physiological time series complexity. Piecewise linear filter multiscale entropy (PLFME) shows improved robustness for heartbeat interval time series analysis.

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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Area of Science:

Physiology
Complexity Science
Biomedical Signal Processing

Background:

Multiscale entropy (MSE) is a standard method for analyzing physiological time series complexity.
The averaging process in MSE can be reinterpreted as applying a piecewise constant filter.

Purpose of the Study:

Introduce filter-based multiscale entropy (FME) for enhanced time series complexity analysis.
Propose piecewise linear filter multiscale entropy (PLFME) for analyzing human heartbeat interval time series.

Main Methods:

Developed FME by filtering time series into frequency components and computing blockwise entropy.
Introduced PLFME, a specialized FME filter inspired by heart rate turbulence theory.
Compared PLFME and adaptive FME with traditional MSE.

Main Results:

FME provides greater flexibility and better captures multiscale information by adapting filters to time series features.
PLFME demonstrates superior robustness to varying data lengths compared to MSE.
Adaptive FME performance is comparable to PLFME, even without prior information.

Conclusions:

FME and its variants offer advanced tools for physiological time series complexity analysis.
PLFME is a robust method for heartbeat interval time series, outperforming MSE.
Adaptive FME presents a promising alternative when prior data characteristics are unknown.