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Method for removing motion artifacts from fNIRS data using ICA and an acceleration sensor.

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This study introduces a new method to remove motion artifacts from functional near-infrared spectroscopy (fNIRS) data by accounting for time delays. The improved technique enhances the accuracy of artifact removal for clearer fNIRS signals.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Functional near-infrared spectroscopy (fNIRS) is widely used for brain activity monitoring.
    • Motion artifacts are a significant challenge in fNIRS data acquisition.
    • Independent Component Analysis (ICA) is a common method for artifact removal, but its effectiveness is limited by time delays in fNIRS signals.

    Purpose of the Study:

    • To develop an improved ICA-based method for removing motion artifacts from fNIRS data.
    • To address the challenge of time-delayed fNIRS signals compared to motion sensor data.
    • To enhance the accuracy of motion artifact removal in fNIRS analysis.

    Main Methods:

    • Proposed a novel ICA-based motion artifact removal technique.
    • Incorporated time-shifting of fNIRS data during multiple ICA applications.
    • Selected the optimal ICA result based on correlation with acceleration sensor data.

    Main Results:

    • The proposed method demonstrated improved accuracy in removing motion artifacts.
    • Accounting for time delays in fNIRS data significantly enhanced artifact identification.
    • The new approach outperforms standard ICA methods that do not consider time shifts.

    Conclusions:

    • The developed time-delay-aware ICA method effectively removes motion artifacts from fNIRS data.
    • This advancement leads to more reliable and accurate fNIRS signal processing.
    • The findings have implications for improving the quality of neuroimaging research using fNIRS.