Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals

TL;DR

This study develops a multi-task machine learning algorithm using fNIRS brain signals to objectively detect pain, emphasizing personalized analysis to account for individual variability.

Contribution

It introduces a multi-task multiple kernel learning approach for pain detection from fNIRS signals, addressing inter-subject variability in pain responses.

Findings

Supports the feasibility of using fNIRS and machine learning for objective pain detection

Highlights the importance of personalized analysis in pain recognition

Demonstrates improved accuracy with multi-task learning approach

Abstract

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.