Automated pain severity detection by using adaptive band-wise GNN technique with EEG signals

Recently, several studies concerning the richness of electroencephalogram (EEG) signals' content to detect pain and classify pain severity have been made. In this study, a novel band-wise adaptive classifier is proposed based on successive brain graphs through five levels of pain. The EEG data were acquired from 44 healthy subjects while putting their hand in the cold water to feel pain continuously and increasingly over time till the intolerable stage, that they withdrew their right hand. During the cold pressor test, participants reported their pain at five different levels while their EEG signals were captured by 32 silver electrodes. The data was decomposed into five frequency bands, and for each band-wise channel, discriminative EEG features were calculated in two different feature sets. Afterward, three connectivity estimators were applied to determine the adjacency matrices for each frequency band and construct the corresponding brain graphs. The constructed graphs, along with feature nodes, were fed into an adaptive graph convolutional neural network (GCNN) based hierarchical classifier, which selects the most discriminative graphs at each node. The discriminability of each estimator was tested by the Kruskal-Wallis test. In each stage of the proposed adaptive hierarchical tree, the most discriminative band-wise GCNN was selected and applied. Based on the proposed classifier, the highest accuracy reached 88.5% in classifying five levels of pain, which outperforms its counterparts.