Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals
© 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 l...
Main Authors: | Lopez-Martinez, Daniel, Peng, Ke, Steele, Sarah C., Lee, Arielle J., Borsook, David, Picard, Rosalind W. |
---|---|
Other Authors: | Massachusetts Institute of Technology. Media Laboratory |
Format: | Article |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
|
Online Access: | https://hdl.handle.net/1721.1/138077 |
Similar Items
-
Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine Learning Approach Using the Wavelet Transform and Bayesian Hierarchical Modeling with Dirichlet Process Priors
by: Lopez-Martinez, Daniel, et al.
Published: (2021) -
Multi-task neural networks for personalized pain recognition from physiological signals
by: Lopez Martinez, Daniel, et al.
Published: (2019) -
Capturing Pain in the Cortex during General Anesthesia: Near Infrared Spectroscopy Measures in Patients Undergoing Catheter Ablation of Arrhythmias.
by: Barry D Kussman, et al.
Published: (2016-01-01) -
Near infrared face recognition using Zernike moments and Hermite kernels
by: Farokhi, Sajad, et al.
Published: (2015) -
Nondestructive classification of mung bean seeds by single kernel near-infrared spectroscopy
by: Kaewkarn Phuangsombut, et al.
Published: (2017-05-01)