Wavelet-based motion artifact removal for functional near-infrared spectroscopy.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technique.
• Motion artifacts are a significant challenge in fNIRS data quality.
• Accurate artifact removal is crucial for reliable fNIRS studies.

Purpose of the Study:

• To develop and evaluate a novel wavelet-based method for motion artifact removal in fNIRS signals.
• To specifically address spike artifacts common in fNIRS recordings.
• To quantify the effectiveness of the proposed method in artifact attenuation and signal distortion.

Main Methods:

• A wavelet-based approach utilizing differences in duration and amplitude between artifacts and fNIRS signals.
• Identification of artifact coefficients based on Gaussian distribution assumptions for hemodynamic signal wavelet coefficients.
• Adaptive selection of wavelet levels for artifact modification based on contamination degree.
• Testing on experimental fNIRS data from infant subjects.

Main Results:

• Demonstrated significant motion artifact energy attenuation: 18.29 dB at 700 nm and 16.42 dB at 830 nm.
• Evaluated on 29 motion events, showing effective artifact reduction.
• Maintained low signal distortion, with normalized mean-square error no more than -16.7 dB in artifact-free regions.

Conclusions:

• The proposed wavelet-based method is effective for removing motion artifacts from fNIRS signals.
• The technique offers a good trade-off between artifact attenuation and signal distortion.
• This method enhances the reliability of fNIRS for brain functional studies, particularly in challenging conditions with infant subjects.