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Linear Circuits01:17

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A linear circuit is characterized by its output having a direct proportionality to its input, adhering to the linearity property, which encompasses the principles of homogeneity (scaling) and additivity. Homogeneity dictates that when the input, also referred to as the excitation, is multiplied by a constant factor, the output, known as the response, is correspondingly scaled by the same constant factor. For instance, if the current is multiplied by a constant 'k,' the voltage likewise...
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Linear mixed-effect models for correlated response to process electroencephalogram recordings.

Vanesa B Meinardi1,2, Juan M Díaz López3,4,5, Hugo Diaz Fajreldines6,5

  • 1I.A.P Ciencias Humanas, Universidad Nacional de Villa María, Arturo Jauretche 1555, 5900 Villa María, Córdoba, Argentina.

Cognitive Neurodynamics
|June 3, 2024
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Summary

Combining permutation entropy and Lempel-Ziv complexity in electroencephalogram (EEG) analysis reveals functional brain changes. Jointly analyzing these metrics offers greater insight than individual measures for identifying distinct brain states.

Keywords:
Correlate responsesElectroencephalographyLempel-Ziv complexityMixed linear modelsPermutation entropy

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Area of Science:

  • Neuroscience
  • Information Theory
  • Biomedical Engineering

Background:

  • Electroencephalogram (EEG) recordings are crucial for understanding brain function.
  • Information theory metrics like permutation entropy and Lempel-Ziv complexity quantify EEG signal complexity.
  • Traditional analysis methods may not fully capture the nuances of EEG data.

Purpose of the Study:

  • To evaluate the efficacy of combining permutation Shannon entropy and permutation Lempel-Ziv complexity for identifying functional changes in EEG signals.
  • To explore the application of Linear Mixed-Effects Models (LMEMs) for simultaneous analysis of multiple EEG metrics.
  • To compare the discriminative power of individual versus joint application of these complexity metrics across different brain states.

Main Methods:

  • Quantification of EEG data from control individuals using permutation Shannon entropy and permutation Lempel-Ziv complexity.
  • Implementation of Linear Mixed-Effects Models (LMEMs) for statistical analysis and hypothesis testing.
  • Comparison of metric performance when used individually versus simultaneously.

Main Results:

  • EEG signals exhibit high variability in both permutation entropy and Lempel-Ziv complexity.
  • A positive correlation was observed between permutation entropy and Lempel-Ziv complexity.
  • Simultaneous analysis of both metrics effectively distinguished between four distinct EEG states (Eyes Closed Wakefulness, Eyes Open Wakefulness, Hyperventilation, Optostimulation).
  • Individual metrics showed limited statistical significance in differentiating some states.

Conclusions:

  • The joint application of permutation Shannon entropy and permutation Lempel-Ziv complexity provides a more comprehensive understanding of EEG functional changes.
  • LMEMs offer a novel approach for simultaneously modeling correlated EEG metrics, advancing neuroscience data analysis.
  • This integrated approach enhances the ability to differentiate between various brain states using EEG.