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

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

Robin Dale^*
, Thomas D. O'Sullivan
, Hamid Dehghani

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A system-derived fully convolutional approach for feature extraction using spatially resolved FD-NIRS signals and linked filter kernels is presented and shown to improve classification of brain activity, as compared to conventional approaches.

Original language	English
Title of host publication	Optical Tomography and Spectroscopy 2022
Publisher	Optica Publishing Group (formerly OSA)
ISBN (Electronic)	9781957171036
DOIs	https://doi.org/10.1364/TRANSLATIONAL.2022.JM3A.71
Publication status	Published - 27 Apr 2022
Event	Clinical and Translational Biophotonics, Translational 2022 - Fort Lauderdale, United States Duration: 24 Apr 2022 → 27 Apr 2022

Publication series

Name	Optics InfoBase Conference Papers
ISSN (Electronic)	2162-2701

Conference

Conference	Clinical and Translational Biophotonics, Translational 2022
Country/Territory	United States
City	Fort Lauderdale
Period	24/04/22 → 27/04/22

Bibliographical note

Funding Information:
Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH) Award Number R01EB029595.

Publisher Copyright:
© 2022 The Author(s).

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

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10.1364/TRANSLATIONAL.2022.JM3A.71Licence: None: All rights reserved

Cite this

@inproceedings{892c541fbd3f42b29593cca8ddc76523,

title = "Intelligent system-driven convolutional feature extraction improves FD-fNIRS imaging and analysis",

abstract = "A system-derived fully convolutional approach for feature extraction using spatially resolved FD-NIRS signals and linked filter kernels is presented and shown to improve classification of brain activity, as compared to conventional approaches.",

author = "Robin Dale and O'Sullivan, \{Thomas D.\} and Hamid Dehghani",

note = "Funding Information: Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH) Award Number R01EB029595. Publisher Copyright: {\textcopyright} 2022 The Author(s).; Clinical and Translational Biophotonics, Translational 2022 ; Conference date: 24-04-2022 Through 27-04-2022",

year = "2022",

month = apr,

day = "27",

doi = "10.1364/TRANSLATIONAL.2022.JM3A.71",

language = "English",

series = "Optics InfoBase Conference Papers",

publisher = "Optica Publishing Group (formerly OSA)",

booktitle = "Optical Tomography and Spectroscopy 2022",

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Dale, R, O'Sullivan, TD & Dehghani, H 2022, Intelligent system-driven convolutional feature extraction improves FD-fNIRS imaging and analysis. in Optical Tomography and Spectroscopy 2022., JM3A.71, Optics InfoBase Conference Papers, Optica Publishing Group (formerly OSA), Clinical and Translational Biophotonics, Translational 2022, Fort Lauderdale, United States, 24/04/22. https://doi.org/10.1364/TRANSLATIONAL.2022.JM3A.71

Intelligent system-driven convolutional feature extraction improves FD-fNIRS imaging and analysis. / Dale, Robin; O'Sullivan, Thomas D.; Dehghani, Hamid.
Optical Tomography and Spectroscopy 2022. Optica Publishing Group (formerly OSA), 2022. JM3A.71 (Optics InfoBase Conference Papers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Intelligent system-driven convolutional feature extraction improves FD-fNIRS imaging and analysis

AU - Dale, Robin

AU - O'Sullivan, Thomas D.

AU - Dehghani, Hamid

N1 - Funding Information: Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH) Award Number R01EB029595. Publisher Copyright: © 2022 The Author(s).

PY - 2022/4/27

Y1 - 2022/4/27

N2 - A system-derived fully convolutional approach for feature extraction using spatially resolved FD-NIRS signals and linked filter kernels is presented and shown to improve classification of brain activity, as compared to conventional approaches.

AB - A system-derived fully convolutional approach for feature extraction using spatially resolved FD-NIRS signals and linked filter kernels is presented and shown to improve classification of brain activity, as compared to conventional approaches.

UR - https://www.scopus.com/pages/publications/85136410415

U2 - 10.1364/TRANSLATIONAL.2022.JM3A.71

DO - 10.1364/TRANSLATIONAL.2022.JM3A.71

M3 - Conference contribution

AN - SCOPUS:85136410415

T3 - Optics InfoBase Conference Papers

BT - Optical Tomography and Spectroscopy 2022

PB - Optica Publishing Group (formerly OSA)

T2 - Clinical and Translational Biophotonics, Translational 2022

Y2 - 24 April 2022 through 27 April 2022

ER -

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

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this