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Currently self-report pain ratings are the gold standard in clinical painassessment. However, the development of objective automatic measures of paincould substantially aid pain diagnosis and therapy. Recent neuroimaging studieshave shown the potential of functional near-infrared spectroscopy (fNIRS) forpain detection. This is a brain-imaging technique that provides non-invasive,long-term measurements of cortical hemoglobin concentration changes. In thisstudy, we focused on fNIRS signals acquired exclusively from the prefrontalcortex, which can be accessed unobtrusively, and derived an algorithm for thedetection of the presence of pain using Bayesian hierarchical modelling withwavelet features. This approach allows personalization of the inference processby accounting for inter-participant variability in pain responses. Our workhighlights the importance of adopting a personalized approach and supports theuse of fNIRS for pain assessment.

Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine  Learning Approach Using the Wavelet Transform and Bayesian Hierarchical  Modeling with Dirichlet Process Priors