Deep learning-based EEG source imaging is robust under varying electrode configurations.

ABSTRACT:

Objectives

Previous research has underscored the necessity of high-density EEG for accurate and reliable EEG source imaging (ESI) results with conventional ESI methods, limiting their utility in clinical settings with only low-density EEG available. In recent years, deep learning-based ESI methods have exhibited robust performance by directly learning spatiotemporal brain activity patterns from data.

Methods

This study investigates the impact of EEG electrode number on a newly proposed Deep Learning-based Source Imaging Framework (DeepSIF). Through computer simulations and clinical data analysis, we assess ESI performance across various channel configurations (16, 21, 32, 64, and 75 channels) comparing DeepSIF and conventional methods against the simulated ground truth and c

SUBMITTER: Rong J 

PROVIDER: S-EPMC12629399 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature