A Machine Learning Framework for EEG-Based Prediction of Treatment Efficacy in Chronic Neck Pain

TL;DR

This paper introduces a machine learning framework utilizing EEG data to predict treatment outcomes in chronic neck pain, aiming to personalize therapy and improve healthcare efficiency.

Contribution

The study develops a comprehensive EEG preprocessing pipeline and reviews existing models to enhance prediction accuracy for treatment efficacy in chronic neck pain.

Findings

Preprocessing pipeline tailored for different EEG types improves data quality.

Literature review of 763 records informs model development.

Framework supports personalized treatment strategies.

Abstract

Chronic neck pain is a leading cause of disability worldwide, and current treatment selection remains largely trial and error. We present a machine learning framework that uses electroencephalography to predict treatment efficacy in patients with chronic neck pain, with the goal of supporting individualized therapy and reducing the burden on healthcare systems. The framework centers on a rigorous data preprocessing stage tailored to the characteristics of each EEG recording type. For resting-state EEG, the preprocessing pipeline comprises baseline signal removal, bad channel identification and exclusion, re-referencing, bandpass and notch filtering, Independent Component Analysis, and power spectral density analysis. For motor execution and motor imagery recordings, the same initial steps are applied, after which signals are aligned to trigger events so that event-related desynchronization (ERD) and event-related synchronization (ERS) can be quantified. Synchronously recorded electromyography data are bandpass filtered and smoothed with a moving average, then correlated with the corresponding EEG channels to characterize the EEG EMG relationship during attempted movement. In parallel, we performed an extensive literature review of machine learning models applied to clinical EEG (763 records initially screened, 16 patient and 47 healthy-control studies retained), to inform the post-processing strategy. Through this combined preprocessing and review effort, we aim to develop a robust predictive model that can support personalized healthcare strategies in chronic pain management.