Multivariate disturbance filtering in auditory fNIRS signals using maximum likelihood gradient estimation method: Feasibility study using sound quality indices

Functional near-infrared spectroscopy (fNIRS) technology has been widely used to analyze biomechanics and diagnose brain activity. Despite being a promising tool for assessing the brain cortex status, this system is susceptible to disturbances and noise from electrical instrumentation and basal meta...

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Bibliographic Details
Published in:Computers in biology and medicine Vol. 179; p. 108840
Main Authors: Yoo, So-Hyeon, Hong, Jiyoung, Hong, Keum-Shik, Lee, Yonghee
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.09.2024
Elsevier Limited
Subjects:
Acoustic Stimulation - methods
Acoustics
Adult
Algorithms
Auditory stimuli
Auditory stimulus
Biomechanics
Brain
Brain research
Comparative analysis
Disturbances
Ergonomics
Feasibility Studies
Female
Functional near-infrared spectroscopy
General linear model
Hearing
Hemodynamic responses
Hemodynamics
Hemoglobin
Humans
Infrared analysis
Infrared filters
Infrared spectra
Infrared spectroscopy
Internal Medicine
Investigations
Likelihood Functions
Loudness
Male
Maximum likelihood estimation
Maximum likelihood generalized extended stochastic gradient
Medical imaging
Metabolism
Methods
Multivariate analysis
Multivariate disturbance parameter
Near infrared radiation
Other
Oxygen saturation
Parameter estimation
Parameter identification
Physiological effects
Physiological responses
Physiology
Psycho-acoustic factor
Respiration
Shivering
Signal processing
Signal Processing, Computer-Assisted
Signal quality
Sound
Sound quality index
Spectroscopy, Near-Infrared - methods
Technology assessment
Wavelet transforms
fNIRS
Multivariate disturbance parameter
Auditory stimulus
mBLL
Sound quality index
GLM
SQI
General linear model
ML-GESG
Psycho-acoustic factor
Functional near-infrared spectroscopy
Maximum likelihood generalized extended stochastic gradient
HbO
HbR
ARMA
modified Beer–Lambert law
autoregressive moving average
oxygenated hemoglobin
deoxygenated hemoglobin
ISSN:0010-4825, 1879-0534, 1879-0534
Online Access:Get full text
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Summary:Functional near-infrared spectroscopy (fNIRS) technology has been widely used to analyze biomechanics and diagnose brain activity. Despite being a promising tool for assessing the brain cortex status, this system is susceptible to disturbances and noise from electrical instrumentation and basal metabolism. In this study, an alternative filtering method, maximum likelihood generalized extended stochastic gradient (ML-GESG) estimation, is proposed to overcome the limitations of these disturbance factors. The proposed algorithm was designed to reduce multiple disturbances originating from heartbeats, breathing, shivering, and instrumental noises as multivariate parameters. To evaluate the effectiveness of the algorithm in filtering involuntary signals, a comparative analysis was conducted with a conventional filtering method, using hemodynamic responses to auditory stimuli and psycho-acoustic factors as quality indices. Using auditory sound stimuli consisting of 12 voice sources (six males and six females), the fNIRS test was configured with 18 channels and conducted on 10 volunteers. The psycho-acoustic factors of loudness and sharpness were used to evaluate physiological responses to the stimuli. Applying the proposed filtering method, the oxygenated hemoglobin concentration correlated better with the psychoacoustic analysis of each auditory stimulus than that of the conventional filtering method. •Method to process fNIRS signals via maximum likelihood gradient estimation proposed.•Designed to reduce multiple disturbances originating from various noise signals.•Comparative analysis was conducted with conventional filtering systems.•In Psycho-acoustic factors, loudness and sharpness, used to evaluate response to stimuli.•fNIRS–SQI analysis can be used to assess psychological effects of human responses.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2024.108840