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Related Experiment Video

Updated: May 14, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

An association framework to analyze dependence structure in time series.

Bilal H Fadlallah1, Austin J Brockmeier, Sohan Seth

  • 1Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA. bhf@cnel.ufl.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary

This study modifies the generalized measure of association (GMA) to reduce temporal effects in time series data. The updated GMA reliably captures EEG channel dependence using time series or envelopes.

Related Experiment Videos

Last Updated: May 14, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Area of Science:

  • Neuroscience
  • Signal Processing
  • Statistical Analysis

Background:

  • Time series analysis in neuroscience often faces challenges due to inherent temporal structures.
  • Quantifying dependence between electroencephalogram (EEG) channels is crucial for understanding brain activity.
  • Existing association methods can be influenced by the temporal correlation within EEG signals.

Purpose of the Study:

  • To propose a modified generalized measure of association (GMA) framework that minimizes the impact of temporal structure in time series.
  • To evaluate the reliability of association methods, including the modified GMA, for detecting dependencies between EEG channels.
  • To compare the effectiveness of using raw EEG time series versus their envelopes for dependence analysis.

Main Methods:

  • Modified the generalized measure of association (GMA) algorithm to reduce temporal structure effects.
  • Generated synthetic data from a Clayton copula to test the modified GMA under controlled conditions.
  • Processed EEG data to extract signal envelopes, mimicking key characteristics of experimental data.
  • Assessed the statistical power and reliability of the modified GMA on both synthetic and real EEG data.

Main Results:

  • The modified GMA procedure effectively captures pairwise dependence between synthetic signals and their envelopes with significant statistical power.
  • The modified GMA demonstrates reliability in assessing dependence between EEG channels.
  • Analysis using GMA and Kendall's tau on EEG signal envelopes aligns with findings from analyses using the raw EEG time series.

Conclusions:

  • The proposed modification to the GMA framework successfully mitigates the influence of temporal structure in time series analysis.
  • The modified GMA provides a reliable method for quantifying dependence between EEG channels, whether using raw time series or extracted envelopes.
  • This approach enhances the accuracy of association methods in neuroscience research, particularly for EEG data analysis.