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Updated: Jun 6, 2026
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Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
Published on: September 3, 2021
Wavelet based motion artifact removal for Functional Near Infrared Spectroscopy.
1Department of Electrical and Computer Engineering, University of British Columbia, Canada.
Summary
This study introduces a new wavelet-based algorithm to remove motion artifacts from functional near-infrared spectroscopy (fNIRS) data. The method effectively reduces artifact power while preserving signal integrity in artifact-free regions.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Functional Near Infrared Spectroscopy (fNIRS) offers unique advantages for brain imaging in challenging environments.
- A significant limitation of fNIRS is its susceptibility to motion artifacts, which can compromise data quality.
- Existing methods for artifact removal may introduce signal distortion.
Purpose of the Study:
- To develop and validate a novel wavelet-based algorithm for robust motion artifact removal in fNIRS signals.
- To assess the effectiveness of the proposed algorithm in attenuating motion artifacts.
- To evaluate the algorithm's impact on the integrity of artifact-free signal components.
Main Methods:
- A new wavelet-based algorithm was designed specifically for motion artifact correction in fNIRS data.
- The algorithm was tested using both simulated fNIRS data and experimentally acquired fNIRS recordings.
- Performance was evaluated by measuring the attenuation of motion artifact power and assessing signal distortion.
Main Results:
- The proposed wavelet-based algorithm demonstrated significant reduction in motion artifact power.
- Average attenuation of 18.97 dB and 15.54 dB was achieved for the two test subjects.
- The method preserved signal quality in artifact-free regions, minimizing unwanted distortion.
Conclusions:
- The developed wavelet-based algorithm is effective in removing motion artifacts from fNIRS signals.
- This technique enhances the reliability of fNIRS data, particularly in studies involving subject movement.
- The algorithm offers a promising solution for overcoming a key limitation in fNIRS research.
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