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Computer Vision-Driven Movement Annotations to Advance fNIRS Pre-Processing Algorithms.

Andrea Bizzego1, Alessandro Carollo1, Burak Senay1

  • 1Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
Summary

A new computer vision (CV) approach accurately detects head movements from videos, providing reliable ground truth for functional near-infrared spectroscopy (fNIRS) research. This automated method aids in developing better motion artifact correction algorithms.

Keywords:
computer visiondeep learningfNIRSfunctional near-infrared spectroscopymotion artifact algorithmsmotion detectionneuroimaging

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Vision

Background:

  • Functional near-infrared spectroscopy (fNIRS) enables brain activity studies in naturalistic settings due to movement tolerance.
  • Motion artifacts in fNIRS data can compromise results, necessitating effective correction algorithms.
  • Evaluating fNIRS motion artifact correction is hindered by the lack of reliable ground truth data for head movements.

Purpose of the Study:

  • To investigate the feasibility and reliability of a deep learning computer vision (CV) approach for automated head movement detection and annotation from video recordings.
  • To establish a reliable ground truth for head movements to aid in the development and evaluation of fNIRS motion artifact correction methods.

Main Methods:

  • Fifteen participants performed controlled head movements across rotational axes at varying speeds and types.
  • Video recordings captured movements; head orientation signals were extracted using SynergyNet.
  • A 1-dimensional UNet (1D-UNet) model processed orientation signals for movement detection, with manual annotations serving as ground truth.

Main Results:

  • The CV model demonstrated strong performance in detecting head movements, with Jaccard indices of 0.954 (training) and 0.865 (test).
  • Consistent performance was observed across different movement axes and speeds.
  • Performance varied by movement type: repeated movements (J=0.941) > complete movements (J=0.872) > half movements (J=0.826).

Conclusions:

  • The proposed CV approach provides accurate and reliable ground truth information for head movements.
  • This automated method can significantly aid researchers in evaluating and improving fNIRS motion artifact correction algorithms.
  • Future research can leverage this CV technique to enhance the quality and validity of fNIRS studies.