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A methodology for validating artifact removal techniques for fNIRS.

Kevin T Sweeney1, Hasan Ayaz, Tomás E Ward

  • 1Department of Electronic Engineering, National University of Ireland Maynooth, Co Kildare, Ireland. ksweeney@eeng.nuim.ie

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

Functional near-infrared spectroscopy (fNIRS) brain monitoring is challenged by motion artifacts. This study introduces a novel dual-channel method to create a clean reference signal, enabling accurate validation of artifact removal techniques for improved brain activity analysis.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Functional near-infrared spectroscopy (fNIRS) is valuable for monitoring brain activity in clinical and home settings.
  • Motion artifacts significantly degrade fNIRS signal quality, hindering clinical interpretation.
  • Current methods lack a reliable way to validate artifact removal technique efficacy.

Purpose of the Study:

  • To propose a novel methodology for fNIRS data collection to validate artifact removal techniques.
  • To enable the creation of a true reference signal for uncontaminated fNIRS data.
  • To facilitate the development and evaluation of signal processing strategies for artifact reduction.

Main Methods:

  • Utilized two fNIRS channels placed in close proximity to sample the same brain region.
  • Introduced motion artifact to only one channel while keeping the adjacent channel artifact-free.
  • Employed an accelerometer-based reference for artifact detection and validation.

Main Results:

  • The proposed methodology successfully generated a true reference signal for motion artifact epochs.
  • Demonstrated the ability to evaluate artifact removal techniques using the generated reference.
  • Validated the methodology's advantage with a simple artifact removal technique.

Conclusions:

  • The novel dual-channel fNIRS data collection method provides a reliable means to validate artifact removal techniques.
  • This approach is crucial for improving the accuracy and reliability of fNIRS-based brain activity monitoring, especially in uncontrolled environments.
  • Facilitates advancements in signal processing for cleaner fNIRS data in clinical and connected health applications.