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Updated: Dec 6, 2025
06:04
Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
Published on: January 17, 2025
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Complexity Analysis on Functional-Near Infrared Spectroscopy Time Series: a Preliminary Study on Mental Arithmetic
Summary
This study introduces topological entropy to analyze functional near-infrared spectroscopy (fNIRS) signals, revealing complex dynamics beyond linear assumptions. Entropy measures offer new insights into brain activity during cognitive tasks.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Complex Systems Theory
Background:
- Physiological systems exhibit complex, nonlinear behaviors.
- Functional near-infrared spectroscopy (fNIRS) analysis often assumes linear system dynamics.
- Traditional fNIRS analysis may not capture the full information in the signals.
Purpose of the Study:
- To investigate topological entropy in fNIRS time series.
- To assess the utility of entropy measures for characterizing fNIRS signal dynamics.
- To explore nonlinear and complexity system theory applications in fNIRS analysis.
Main Methods:
- Collected fNIRS data from 10 healthy subjects during a mental arithmetic task.
- Utilized sample entropy and fuzzy entropy to quantify time series irregularity.
- Employed distribution entropy to measure time series complexity.
Main Results:
- Entropy estimates provide complementary characterization of fNIRS dynamics.
- Findings suggest nonlinear methods offer richer insights than traditional time-domain measurements.
- Demonstrated the potential of complexity measures in fNIRS signal analysis.
Conclusions:
- Topological entropy analysis enhances understanding of fNIRS signal complexity.
- Nonlinear and complexity system theory can advance fNIRS functional activation studies.
- This approach opens new avenues for investigating brain activity with fNIRS.
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