Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction

Abstract

An effective physiological pain assessment method that complements the gold standard of self-report is highly desired in pain clinical research and practice. Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity. Existing EEG-based pain assessment is normally achieved by cross-individual prediction (i.e., to train a prediction model from a group of individuals and to apply the model on a new individual), so its performance is seriously hampered by the substantial inter-individual variability in pain-evoked EEG responses. In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered. Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability. In addition, a nonlinear relationship between the level of pain perception and pain-evoked EEG responses was obtained, which inspired us to further develop a new two-stage pain prediction strategy, a binary classification of low-pain and high-pain trials followed by a continuous prediction for high-pain trials only, both of which used spontaneous-EEG-normalized magnitudes of evoked EEG responses as features. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses, and the two-stage pain prediction method can lead to a higher prediction accuracy.

Keywords: cross-individual prediction; normalization; pain prediction; pain-evoked EEG; spontaneous EEG.

Figure 1

Correlation between the mean and SD of RMS_S and RMS_P at levels of (A) NRS ≤ 4, and (B) NRS > 4. Red dots represent the mean or SD of RMS_S and RMS_P, which are averaged across all trials at each pain intensity level for each participant. Gray lines represent the best linear fit.

Figure 2

(A) Relationship between pain ratings and RMS_P (from one participant). Colored dots represent mean ± SD of RMS_P averaged across trials at different level of pain perception. The red line represents the fitted global linear model, while the blue lines represent the fitted two-piecewise linear model. (B) Comparison of MSE (mean ± SD) of all participants between two fitting models.

Figure 3

Effects of sEEG normalization on inter-individual variability of (A) binary classification thresholds, (B) slopes of linear regression models for high-pain trials, (C) intercepts of linear regression models for high-pain trials. Noted that mean values were removed from these parameters for illustration. The box plots show the minimu, lower quartile, median, upper quartile, and maximum values of one group of variables.