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Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short

So-Hyeon Yoo1, Guanghao Huang2, Keum-Shik Hong1,2

  • 1School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a novel functional near-infrared spectroscopy (fNIRS) method to identify brain activity without a known trial period. It effectively predicts and removes physiological noise during task sessions, improving brain-computer interface applications.

Keywords:
filteringfunctional near-infrared spectroscopylong-short term memorymaximal overlap discrete wavelet transformphysiological noise

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Functional near-infrared spectroscopy (fNIRS) typically identifies activated brain channels using a desired hemodynamic response function (dHRF) derived from a known trial period.
  • This conventional method is not applicable when the trial period is unknown, posing a challenge for real-time brain-computer interfaces.
  • Estimating the brain signal's starting time is crucial for passive brain-computer interfaces.

Purpose of the Study:

  • To propose an innovative fNIRS method for identifying activated channels without relying on a predefined trial period or dHRF.
  • To develop a technique for predicting and subtracting physiological noise during task sessions, even with unknown trial periods.
  • To offer an alternative solution for fNIRS analysis when conventional methods are inapplicable due to noise interference.

Main Methods:

  • Utilizes maximal overlap discrete wavelet transform (MODWT) to extract fluctuating signals from resting-state fNIRS data.
  • Identifies low-frequency wavelets associated with physiological noise and trains them using long-short term memory (LSTM) networks.
  • Decomposes resting-state data into nine wavelets, employing the fifth to ninth for learning and prediction, and extends the signal from resting to task states to maintain phase information.

Main Results:

  • The proposed method successfully predicts and subtracts physiological noise during task sessions without requiring a dHRF.
  • The eighth wavelet decomposition showed the largest prediction error difference between methods with and without dHRF within a 15-s window.
  • This technique offers a viable alternative for fNIRS analysis when physiological noise complicates activation period detection.

Conclusions:

  • The developed method provides a robust approach for fNIRS signal analysis in scenarios with unknown trial periods.
  • It effectively addresses the challenge of physiological noise, particularly when its frequency overlaps with neural activation signals.
  • This innovation holds significant potential for advancing passive brain-computer interface applications by enabling accurate brain signal detection.