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Published on: July 1, 2015
Performance of different synchronization measures in real data: a case study on electroencephalographic signals
R Quian Quiroga1, A Kraskov, T Kreuz
1John von Neumann Institute for Computing, Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany. rodri@vis.caltech.edu
Summary
This study quantifies synchronization in rat brain electroencephalography (EEG) signals using multiple methods. Results indicate these measures are valuable for analyzing real-world EEG data and may have clinical relevance.
Area of Science:
- Neuroscience
- Biophysics
- Signal Processing
Background:
- Brain activity involves complex interactions between hemispheres.
- Electroencephalography (EEG) is a key tool for studying brain dynamics.
- Quantifying interhemispheric synchronization is crucial for understanding brain function.
Purpose of the Study:
- To evaluate various synchronization measures for analyzing rat EEG.
- To compare the effectiveness of nonlinear interdependences, phase synchronization, mutual information, cross-correlation, and coherence.
- To assess the clinical relevance of these measures for EEG signal analysis.
Main Methods:
- Analysis of electroencephalographic (EEG) signals from rat left and right hemispheres.
- Application of multiple synchronization quantification techniques: nonlinear interdependences, phase synchronization measures, mutual information, cross-correlation, and coherence function.
- Comparison of results across different synchronization measures.
Main Results:
- Most applied synchronization measures provided useful quantification of EEG data, which was not obvious from raw signals.
- Despite methodological differences, synchronization measures yielded qualitatively similar results.
- A close relationship between two phase synchronization measures was identified and one was extended.
Conclusions:
- The studied synchronization measures are valuable tools for analyzing real-world data, particularly rat EEG.
- These measures offer insights into brain dynamics that are not readily apparent from raw EEG.
- The application of these measures as complementary variables in EEG analysis could hold clinical significance.
