Cancellation of Superficial Blood Flow in Brain Function Measurements Using Near-Infrared Spectroscopy.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Optical Imaging

Background:

• Near-infrared spectroscopy (NIRS) is crucial for non-invasive brain function measurement.
• Superficial blood flow causes signal fluctuations, hindering accurate brain activity detection.
• Effective noise reduction techniques are essential for reliable NIRS data.

Purpose of the Study:

• To develop and evaluate a novel sensor arrangement for cancelling superficial blood flow in NIRS.
• To assess the efficacy of Independent Components Analysis (ICA) in correcting NIRS signals using this configuration.
• To improve the accuracy of brain function measurements by mitigating motion artifacts.

Main Methods:

• Proposed an equilateral-triangle sensor configuration with short-distance detectors.
• Applied Independent Components Analysis (ICA) to the sensor data.
• Measured blood volume changes due to motion artifacts from posture changes.
• Analyzed the signal correction effectiveness of ICA compared to simple subtraction.

Main Results:

• The equilateral triangle sensor arrangement effectively cancels superficial blood flow.
• Independent Components Analysis (ICA) significantly improved the correction of NIRS signals.
• ICA demonstrated superior performance compared to simple subtraction methods.
• The ICA method showed minimal dependence on measurement conditions.

Conclusions:

• The proposed equilateral triangle sensor configuration combined with ICA is effective for reducing superficial blood flow artifacts in NIRS.
• This approach enhances the reliability and accuracy of brain function measurements.
• The method is robust and applicable across various measurement conditions.