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5 March 2021 fNIRS signal quality estimation by means of a machine learning algorithm trained on morphological and temporal features
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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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© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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