Electroencephalography-Based Pain Detection Using Kernel Spectral Connectivity Network with Preserved Spatio-Frequency Interpretability

Chronic pain leads to not only physical discomfort but also psychological challenges, such as depression and anxiety, which contribute to a substantial healthcare burden. Pain detection and assessment remains a challenge due to its subjective nature. Current clinical methods may be inaccurate or unfeasible for non-verbal patients. Consequently, Electroencephalography (EEG) has emerged as a promising non-invasive tool for pain detection. However, EEG-based pain detection faces challenges such as noise, volume conduction effects, and high inter-subject variability. Deep learning (DL) models have shown potential in overcoming these challenges by extracting nonlinear and discriminative patterns. Despite advancements, these models often require a subject-dependent approach and lack of interpretability. To address these limitations, we propose a threefold DL-based framework for coding EEG-based pain detection patterns. (i) We employ the Kernel Cross-Spectral Gaussian Functional Connectivity Network (KCS-FCnet) to code pairwise channel dependencies for pain detection. (ii) Furthermore, we introduce a frequency-based strategy for class activation mapping to visualize pertinent pain EEG features, thereby enhancing visual interpretability through spatio-frequency patterns. (iii) Further, to account for subject variability, we conduct cross-subject analysis and grouping, clustering individuals based on similar pain detection performance, functional connectivity patterns, sex, and age. We evaluate our model using the Brain Mediators of Pain dataset and demonstrate its robustness through subject-dependent and cross-subject generalization tasks for pain detection on non-verbal patients.