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Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

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A novel nonlinear bispectrum analysis for dynamical complex oscillations.

Yidong Hu¹, Wenbin Shi¹, Chien-Hung Yeh¹

¹Beijing Institute of Technology, Beijing, 100081 China.

Cognitive Neurodynamics

|November 13, 2024

Summary

This summary is machine-generated.

This study introduces a novel bispectrum method using uniform phase empirical mode decomposition (UPEMD) to analyze brain activity complexity and power. The method shows potential for reflecting gait improvements in Parkinson's disease patients.

Keywords:

Electrophysiological oscillation Gait Lempel–Ziv complexity Parkinson's disease UPEMD-based bispectrum

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A Computational Method to Quantify Fly Circadian Activity

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Published on: October 28, 2017

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Gait disturbance is a significant challenge in Parkinson's disease (PD) management, particularly for adaptive deep brain stimulation (DBS).
Analyzing complex oscillations in deep brain activity is crucial for understanding and potentially treating PD symptoms.

Purpose of the Study:

To propose a novel bispectrum method for constructing frequency power and complexity spectra.
To evaluate the method's performance in analyzing nonlinear and non-stationary signals, including DBS recordings from PD patients.

Main Methods:

Uniform Phase Empirical Mode Decomposition (UPEMD) for nonlinear signal extraction and explicit frequency identification.
Lepel-Ziv Complexity (LZC) and frequency power per mode for comprehensive spectral analysis.
Simulations using chaotic maps and non-stationary signals, followed by analysis of DBS data from PD patients.

Main Results:

The UPEMD-based LZC spectrum effectively distinguishes signal complexity, reflecting bandwidth and noise levels.
The UPEMD-based power spectrum reveals power distribution in nonlinear, non-stationary oscillations across frequencies.
Auditory cues modulated DBS recording complexity in PD patients (45-60 Hz), with decreased power in higher frequencies (>30 Hz) during cue presentation.

Conclusions:

The proposed bispectrum method, combining LZC and power spectrum analysis, offers refined resolution for examining targeted frequencies.
The findings suggest the method's potential in objectively reflecting gait improvements in Parkinson's disease patients.
This approach provides a novel tool for analyzing complex neural oscillations and their modulation by external stimuli.