Photo Credit: yacobchuk

The following is a summary of “Resting-state frontal electroencephalography (EEG) biomarkers for detecting the severity of chronic neuropathic pain,” published in the August 2024 issue of Pain by Ryu et al.

Frontal channel EEG-based signals can measure pain through spectral analysis and phase-amplitude coupling. 

Researchers conducted a retrospective study to determine frontal channel EEG-based biomarkers for quantifying pain severity, focusing on band-power features and evaluating the viability of complex features using machine learning algorithms. 

They examined an EEG dataset of 36 patients with chronic pain during an eyes-open resting state. They conducted a correlation analysis between clinically labeled pain scores and EEG features from Fp1 and Fp2 channels, including EEG band-powers, phase-amplitude couplings (PAC), and their asymmetry features.

The results showed significant correlations between beta power asymmetry (r = −0.375), gamma power asymmetry (r = −0.433), and low beta to low gamma coupling (r = −0.397) with pain scores. In contrast, band power features showed no results. Support Vector Regression with a polynomial kernel revealed a promising performance (R-squared value = 0.655), suggesting a regression of pain intensity within a clinically usable error range. The 4 most selected features were recognized as gamma power asymmetry, PAC asymmetry of theta to low gamma, and low beta to low/high gamma.

They concluded that complex features like asymmetry and phase-amplitude coupling were essential in pain research, and frontal channel-based EEG could evaluate pain intensity.

Source: nature.com/articles/s41598-024-71219-3#Abs1