MFDFormer: A Unified Multiscale Frequency Domain MetaFormer Framework for EEG-Based Chronic Pain Recognition

Shihan Qiu, Kan Xie, Junjie Yang, Qiyu Yang, Bo Zhang, Yuhong Gu, Feng Xie, Rongjian Qiu, Shengli Xie, Wenfang Bai

Abstract: Wearable electroencephalogram (EEG) devices have shown great potential in enabling real-time monitoring of subtle changes in brain activity, providing new possibilities for the assessment and management of chronic pain. However, recognizing pain-related biomarkers from EEG data remains a complex, multitask challenge. Most existing research focuses on single-task approaches and rarely addresses this issue within a unified framework. In this article, we propose a novel deep neural network (DNN) model called multiscale frequency domain MetaFormer (MFDFormer), which is designed to simultaneously predict the presence, type, and intensity of chronic pain. The proposed MFDFormer comprises two primary subnetworks: 1) multiscale feature extractor (MFE) and 2) frequency domain MetaFormer (FDFormer) encoder. The MFE extracts diverse EEG features through convolutions with different kernel sizes, while a self-attention mechanism is integrated into MFE to emphasize the importance of interdependency among these features. The FDFormer encoder refines the output MFE features using causal convolutions to capture local patterns and projects them into a higher dimensional representation domain. Additionally, it incorporates spatial and temporal frequency domain learners (TFDLs) in parallel to effectively capture the spatial-temporal information of EEG data. Based on the publicly available brain function in chronic pain (BFCP) dataset, the proposed MFDFormer demonstrates superior performance over state-of-the-art algorithms, achieving accuracies of 97.59%, 96.02%, and 83.48% in pain or nonpain state classification (P/NSC), pain type classification (PTC), and pain intensity classification (PIC) tasks, respectively. This article proposes a unified, end-to-end DNN-based framework for multitask chronic pain recognition, providing a reliable solution with the potential to advance pain diagnosis and management in IoT and smart wearable applications.