fNIRS signal quality estimation by means of a machine learning algorithm trained on morphological and temporal features

M. Sofía Sappia; Naser Hakimi; Liucija Svinkunaite; Thomas Alderliesten; Jörn M. Horschig; Willy N. J. M. Colier

doi:10.1117/12.2587188

5 March 2021 fNIRS signal quality estimation by means of a machine learning algorithm trained on morphological and temporal features

M. Sofía Sappia, Naser Hakimi, Liucija Svinkunaite, Thomas Alderliesten, Jörn M. Horschig, Willy N. J. M. Colier

Author Affiliations +

Proceedings Volume 11638, Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables II; 116380F (2021) https://doi.org/10.1117/12.2587188
Event: SPIE BiOS, 2021, Online Only

Abstract

Functional near infrared spectroscopy (fNIRS) is used for brain hemodynamic assessment. Cortical hemodynamics are reliably estimated when the recorded signal has a sufficient quality. This is acquired when fNIRS optodes have proper scalp coupling. A lack of proper scalp coupling causes false positives and false negatives. Therefore, developing an objective algorithm for determining fNIRS signal quality is of great importance. In this study, we developed a machine learning-based algorithm for quantitatively rating fNIRS signal quality. Our promising results confirm the efficacy of the algorithm in determining fNIRS signal quality and hence decreasing misinterpretations.

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M. Sofía Sappia, Naser Hakimi, Liucija Svinkunaite, Thomas Alderliesten, Jörn M. Horschig, and Willy N. J. M. Colier "fNIRS signal quality estimation by means of a machine learning algorithm trained on morphological and temporal features", Proc. SPIE 11638, Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables II, 116380F (5 March 2021); https://doi.org/10.1117/12.2587188

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