Resting-state frontal electroencephalography (EEG) biomarkers for detecting the severity of chronic neuropathic pain.

ABSTRACT: Increasing evidence is present to enable pain measurement by using frontal channel EEG-based signals with spectral analysis and phase-amplitude coupling. To identify frontal channel EEG-based biomarkers for quantifying pain severity, we investigated band-power features to more complex features and employed various machine learning algorithms to assess the viability of these features. We utilized a public EEG dataset obtained from 36 patients with chronic pain during an eyes-open resting state and performed correlation analysis between clinically labelled pain scores and EEG features from Fp1 and Fp2 channels (EEG band-powers, phase-amplitude couplings (PAC), and its asymmetry features). We also conducted regression analysis with various machine learning models to predict patients' pain int

SUBMITTER: Ryu S 

PROVIDER: S-EPMC11364843 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature