  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.
