Sliding-window motion artifact rejection for Functional Near-Infrared Spectroscopy

Area of Science:

• Neuroscience
• Biomedical Engineering
• Optical Imaging

Background:

• Functional Near-Infrared Spectroscopy (fNIR) is a noninvasive optical brain monitoring technique.
• It measures hemodynamic responses by tracking changes in oxygenated and deoxygenated hemoglobin.
• fNIR enables portable and safe brain activity monitoring in natural environments.

Purpose of the Study:

• To address challenges in fNIR signal quality due to motion artifacts.
• To develop an automated method for identifying and excluding noisy data segments caused by head motion and poor sensor coupling.
• To ensure suitability for real-time fNIR applications.

Main Methods:

• Development of a simple, iterative automated preprocessing method.
• Focus on identifying motion artifact-related noise, particularly from sensor coupling issues.
• Algorithm designed for efficient real-time data processing.

Main Results:

• Successful identification and exclusion of motion artifact-corrupted data segments.
• Demonstrated improvement in fNIR signal quality.
• Method validated for its suitability in real-time applications.

Conclusions:

• The developed automated method effectively reduces noise in fNIR signals caused by motion artifacts.
• This technique enhances the reliability of fNIR for ambulatory and field monitoring.
• The method is practical for real-time noise reduction in fNIR data.