1. fNIRS signal quality estimation by means of a machine learning algorithm trained on morphological and temporal features
- Author
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Sappia, M.S., Hakimi, N., Svinkunaite, L., Alderliesten, T., Horschig, J.M., Colier, W.N.J.M., Shadgan, B., Gandjbakhche, A.H., Shadgan, B., and Gandjbakhche, A.H.
- Subjects
business.industry ,Computer science ,False positives and false negatives ,Cognitive artificial intelligence ,Machine learning ,computer.software_genre ,Signal ,Signal-to-noise ratio ,Signal quality ,Functional near-infrared spectroscopy ,functional near infrared spectroscopy, signal quality assessment, signal quality quantification, signal to noise ratio ,Artificial intelligence ,business ,Algorithm ,computer - Abstract
Item does not contain fulltext 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. SPIE BiOS (6-12 March, 2021)
- Published
- 2021
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