Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals
Name
1808.06774.pdf
Description
Accepted version
Size
1.79 MB
Format
Adobe PDF
Checksum (MD5)
07ac746f3746ef08d5d205f8a5aa53e4
Author(s)
Lopez-Martinez, Daniel
Peng, Ke
Steele, Sarah C.
Lee, Arielle J.
Borsook, David
Picard, Rosalind W.
Date Issued
August 2018
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Lopez-Martinez, Daniel, Peng, Ke, Steele, Sarah C., Lee, Arielle J., Borsook, David et al. 2018. "Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals."
Version
Author's final manuscript
Abstract
© 2018 IEEE. Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure alterations in brain function in evoked and ongoing pain. In this study, we applied multi-task machine learning methods to derive a practical algorithm for pain detection derived from fNIRS signals in healthy volunteers exposed to a painful stimulus. Especially, we employed multi-task multiple kernel learning to account for the inter-subject variability in pain response. Our results support the use of fNIRS and machine learning techniques in developing objective pain detection, and also highlight the importance of adopting personalized analysis in the process.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Harvard-MIT Program in Health Sciences and Technology
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icpr.2018.8545823
