Multivariate synchronization curve: A measure of synchronization in different multivariate signals
Binbin Shang1, Pengjian Shang1
1Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing 100044, People's Republic of China.
Chaos (Woodbury, N.Y.)
|January 1, 2022
Summary
This study introduces an extended multivariate synchronization index (MSI) using escort distributions. This enhanced method provides a more comprehensive analysis of signal synchronization, improving brain-computer interface applications.
Area of Science:
- Signal processing
- Neuroscience
- Biomedical engineering
Background:
- The multivariate synchronization index (MSI) is crucial for frequency recognition in brain-computer interfaces.
- Existing MSI methods provide a single value, potentially limiting comprehensive signal analysis.
Purpose of the Study:
- To generalize the MSI by incorporating escort distributions.
- To develop a more sensitive and comprehensive method for measuring signal synchronization.
Main Methods:
- Generalized MSI using escort distributions, creating a multivariate synchronization curve.
- Applied the extended MSI to both simulated and real-world datasets.
- Compared the extended MSI with the original MSI (q=1).
Main Results:
- The extended MSI effectively captures complex signal relationships.
- The multivariate synchronization curve provides richer information than a single MSI value.
- Demonstrated improved signal synchronization measurement compared to the standard MSI.
Conclusions:
- The generalized MSI offers a more comprehensive and potentially practical approach to multivariate signal synchronization.
- This method enhances information extraction from different signals.
- The extended MSI shows promise for advanced brain-computer interface applications.
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