Intelligent system-driven convolutional feature extraction improves FD-fNIRS imaging and analysis

Robin Dale; Thomas D. O’Sullivan; Hamid Dehghani

doi:10.1364/TRANSLATIONAL.2022.JM3A.71

Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN)
Technical Digest Series (Optica Publishing Group, 2022),
paper JM3A.71
•https://doi.org/10.1364/TRANSLATIONAL.2022.JM3A.71

Intelligent system-driven convolutional feature extraction improves FD-fNIRS imaging and analysis

Robin Dale, Thomas D. O’Sullivan, and Hamid Dehghani

Optical Tomography and Spectroscopy 2022

Fort Lauderdale, Florida United States
24–27 April 2022
ISBN: 978-1-957171-03-6

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Joint Poster Session I On Site (JM3A)

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R. Dale, T. D. O’Sullivan, and H. Dehghani, "Intelligent system-driven convolutional feature extraction improves FD-fNIRS imaging and analysis," in Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper JM3A.71.

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Abstract

A system-derived fully convolutional approach for feature extraction using spatially resolved FD-NIRS signals and linked filter k ernels i s p resented a nd s hown to improve classification of brain activity, as compared to conventional approaches.

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