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¹H NMR: Interpreting Distorted and Overlapping Signals

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Related Experiment Video

Updated: Oct 22, 2025

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

Estimating Phase Amplitude Coupling between Neural Oscillations Based on Permutation and Entropy.

Liyong Yin¹, Fan Tian², Rui Hu²

¹Department of Internal Medicine, Hebei Medical University, Shijiazhuang 050011, China.

Entropy (Basel, Switzerland)

|August 27, 2021

Summary

This summary is machine-generated.

Symbolic joint entropy (SJE) is an efficient method for measuring cross-frequency phase-amplitude coupling (PAC) in neural oscillations. SJE accurately identifies PAC frequency ranges, even with noise, offering superior computational performance.

Keywords:

entropy mutual information neuronal oscillations permutation phase–amplitude coupling

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Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Cross-frequency phase-amplitude coupling (PAC) is crucial for understanding neuronal network interactions.
PAC reflects the interplay between the phase of low-frequency oscillations (LFOs) and the amplitude of high-frequency oscillations (HFOs).

Purpose of the Study:

To evaluate and compare four permutation analysis-based methods for measuring PAC.
To identify the most computationally efficient and accurate method for PAC assessment in neural data.

Main Methods:

Applied four permutation analysis methods: multiscale permutation mutual information (MPMI), permutation conditional mutual information (PCMI), symbolic joint entropy (SJE), and weighted-permutation mutual information (WPMI).
Evaluated method performance using simulation data, analyzing effects of coupling strength, signal-to-noise ratios (SNRs), and data length.
Assessed accuracy in identifying PAC frequency ranges under spike noise interference.

Main Results:

Symbolic joint entropy (SJE) demonstrated comparable PAC strength measurement performance to other methods.
SJE exhibited significantly higher computational efficiency compared to MPMI, PCMI, and WPMI.
SJE accurately identified PAC frequency bands even in the presence of spike noise.

Conclusions:

Symbolic joint entropy (SJE) is a highly effective and computationally efficient method for evaluating cross-frequency phase-amplitude coupling in neural oscillations.
SJE offers robust performance in noisy conditions, making it a preferred choice for analyzing neural oscillatory interactions.