EEG connectivity features associated with fibromyalgia revealed by machine learning.

ABSTRACT:

Introduction

We present connectivity-based features associated with fibromyalgia, derived from raw EEG data at the sensor level.

Methods

These connectivity features were identified through a data-driven method, employing machine learning. We carried out some automatic, moderate pre-processing and extracted spectral connectivity features. Machine learning experiments then followed, employing feature importance analyses and feature selection techniques for building high-performing classification models; finally, based on robust cross-validation and test evaluation, we obtained the features associated with fibromyalgia. The raw EEG signals from 463 participants are used in the primary analysis. An external dataset that consists of 48 participants is used to validate the identif

SUBMITTER: Li J 

PROVIDER: S-EPMC12852373 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature