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Currently there is no validated objective measure of pain. Recentneuroimaging studies have explored the feasibility of using functionalnear-infrared spectroscopy (fNIRS) to measure alterations in brain function inevoked and ongoing pain. In this study, we applied multi-task machine learningmethods to derive a practical algorithm for pain detection derived from fNIRSsignals in healthy volunteers exposed to a painful stimulus. Especially, weemployed multi-task multiple kernel learning to account for the inter-subjectvariability in pain response. Our results support the use of fNIRS and machinelearning techniques in developing objective pain detection, and also highlightthe importance of adopting personalized analysis in the process.

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