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Updated: Apr 18, 2026

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
Published on: October 6, 2023
Activation detection in functional near-infrared spectroscopy by wavelet coherence
Xin Zhang1, Jian Yu2, Ruirui Zhao2
1Chinese Academy of Sciences, Institute of Automation, Brainnetome Center, Beijing 100190, China.
This study introduces a novel multiscale wavelet coherence analysis to improve the detection of brain activity using functional near-infrared spectroscopy (fNIRS). The method effectively identifies true hemodynamic responses amidst physiological noise in fNIRS data.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Functional near-infrared spectroscopy (fNIRS) measures brain activity via hemodynamic responses.
- fNIRS signals are often contaminated by physiological noise, complicating accurate analysis.
- Distinguishing true neural activation from noise is a significant challenge in fNIRS studies.
Purpose of the Study:
- To develop an advanced method for enhanced detection of brain activation from fNIRS data.
- To overcome limitations posed by physiological noise in fNIRS signal analysis.
- To improve the reliability of identifying task-related hemodynamic responses.
Main Methods:
- A multiscale analysis approach utilizing wavelet coherence was designed.
- The experimental task paradigm was converted into a binary signal.
- Wavelet coherence investigated the relationship between predicted hemodynamic responses and fNIRS data.
- Summing coherence in the time-frequency domain quantified activation levels per channel.
Main Results:
- The proposed method demonstrated effectiveness in simulated fNIRS data.
- The technique successfully identified activated channels obscured by noise in experimental fNIRS data.
- The multiscale wavelet coherence analysis improved the signal-to-noise ratio for detecting brain activity.
Conclusions:
- The developed multiscale wavelet coherence analysis is a robust tool for fNIRS data.
- This method enhances the accuracy of detecting task-related brain activation.
- The approach offers a promising solution for overcoming noise challenges in neuroimaging.
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