Prediction of pain perception using multivariate pattern analysis of laser-evoked EEG oscillations

This paper is aimed to predict pain perception from laser-evoked EEG oscillatory activities in the time-frequency domain with multivariate pattern analysis (MVPA). We first identify pre-/post-stimulus EEG oscillatory activities that are correlated with the intensity of laser-evoked pain perception using a multivariate linear regression (MVLR) model, which is solved by partial least-squares regression (PLSR). Further, we used the MVLR model to predict the intensity of pain perception from identified pain-correlated time-frequency EEG data for each subject. Our results showed that the proposed MVLR prediction model provided a qualitative prediction of pain (classification of low pain and high pain) with an accuracy of 78.53 ± 1.16% and a quantitative prediction of pain (on a continuous scale from 0 to 10) with a mean absolute error (MAE) of 1.45 ± 0.05, both of which are significantly better than the results of the conventional pain prediction based on single-trial detection of laser-evoked potentials. Besides, for the first time it was found that the pre-stimulus EEG oscillation could significantly contribute to the prediction, which extended our notion of the determinants of pain perception.