A novel approach to predict subjective pain perception from single-trial laser-evoked potentials

Abstract

Pain is a subjective first-person experience, and self-report is the gold standard for pain assessment in clinical practice. However, self-report of pain is not available in some vulnerable populations (e.g., patients with disorders of consciousness), which leads to an inadequate or suboptimal treatment of pain. Therefore, the availability of a physiology-based and objective assessment of pain that complements the self-report would be of great importance in various applications. Here, we aimed to develop a novel and practice-oriented approach to predict pain perception from single-trial laser-evoked potentials (LEPs). We applied a novel single-trial analysis approach that combined common spatial pattern and multiple linear regression to automatically and reliably estimate single-trial LEP features. Further, we adopted a Naïve Bayes classifier to discretely predict low and high pain and a multiple linear prediction model to continuously predict the intensity of pain perception from single-trial LEP features, at both within- and cross-individual levels. Our results showed that the proposed approach provided a binary prediction of pain (classification of low pain and high pain) with an accuracy of 86.3 ± 8.4% (within-individual) and 80.3 ± 8.5% (cross-individual), and a continuous prediction of pain (regression on a continuous scale from 0 to 10) with a mean absolute error of 1.031 ± 0.136 (within-individual) and 1.821 ± 0.202 (cross-individual). Thus, the proposed approach may help establish a fast and reliable tool for automated prediction of pain, which could be potentially adopted in various basic and clinical applications.

Gan Huang

My research interests include Neural Modulation, Brain Computer Interface and Neural Prosthetics.