A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking

Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not cor...

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Vydané v:Sensors (Basel, Switzerland) Ročník 21; číslo 15; s. 5117
Hlavní autori: Perpetuini, David, Cardone, Daniela, Filippini, Chiara, Chiarelli, Antonio Maria, Merla, Arcangelo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 28.07.2021
MDPI
Predmet:
Algorithms
Decomposition
functional near infrared spectroscopy (fNIRS)
Hemodynamics
infrared imaging
infrared thermography
Motion
motion artefacts
Noise
Physiology
Principal components analysis
Sensors
Spectroscopy, Near-Infrared
Thermography
Wavelet Analysis
wavelet coherence
wavelet transform
Wavelet transforms
infrared thermography
infrared imaging
wavelet transform
functional near infrared spectroscopy (fNIRS)
motion artefacts
tracking algorithms
wavelet coherence
ISSN:1424-8220, 1424-8220
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Shrnutí:Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes’ movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes’ movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes’ movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.
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These authors equally contributed.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21155117