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This study introduces a new algorithm for calculating instantaneous correlation between signals, enabling detailed analysis of time-varying coordination using correlation map analysis (CMA). This method reveals dynamic signal relationships, even with temporal fluctuations.

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Area of Science:

Signal processing
Data analysis
Computational neuroscience

Background:

Analyzing time-varying coordination between signals is crucial in many scientific fields.
Existing methods may struggle with dynamic temporal fluctuations and require complex parameter tuning.

Purpose of the Study:

To present a novel algorithm for computing instantaneous correlation coefficients between two signals.
To introduce Correlation Map Analysis (CMA) as a tool for assessing time-varying signal coordination.
To demonstrate the algorithm's utility in analyzing complex spatio-temporal patterns.

Main Methods:

Developed an algorithm for instantaneous correlation coefficient computation.
Algorithm features tunable filter-like properties and unidirectional/bidirectional application.
Generated 2D correlation maps visualizing correlation as a function of time and temporal offset.

Main Results:

The algorithm effectively computes instantaneous correlation, capturing dynamic signal coordination.
Correlation Map Analysis (CMA) provides rapid visual assessment of evolving correspondence patterns.
Demonstrated utility in analyzing spatio-temporal coordination in linguistic performance (audible and visible components).

Conclusions:

The presented algorithm and CMA offer a robust method for analyzing time-varying signal coordination.
The approach is sensitive to temporal fluctuations and adaptable through a single parameter.
Effective visualization via correlation maps facilitates understanding of complex dynamic relationships.