Multimodal EEG–Wearable Integration for Generalizable Pain Classification

Objective pain assessment is challenging due to the inherently subjective and multidimensional nature of pain, compounded by intersubject variability and noise in naturalistic settings. These factors limit the development of reliable digital biomarkers for clinical decision support and remote monitoring. In this work, we propose a hybrid deep learning architecture that performs early fusion of single channel EEG band power streams with peripheral biosignals recorded by a wristband (electrodermal activity, photoplethysmography/BVP, triaxial accelerometry, and skin temperature). The model uses residual 1D CNN encoders to learn EEG representations and a multilayer perceptron to encode wearable features; the two embeddings are fused in a unified layer to produce predictions for four classes under a strict subject independent evaluation protocol. On PhysioPain, the proposed approach achieves 86.98% accuracy and 87.46% weighted F1, surpassing widely used machine learning baselines, while maintaining an average inference latency of 0.1495 ms per sample for real time operation. In addition to widely used machine learning baselines, we benchmark contemporary deep 1D CNN baselines for a fair comparison: the proposed model improves over 1D DenseNet by 4.49 percentage points in accuracy and 4.70 percentage points in weighted F1, and over 1D ResNet by 7.90 percentage points in accuracy and 7.93 percentage points in weighted F1. To our knowledge, this is the first study to perform subject independent multiclass classification of natural pain phenotypes with explicit handling of class imbalance by early fusion of MindWave EEG with Empatica E4 signals, representing a step toward generalizable and clinically useful digital biomarkers of pain.