Improving TMS EEG Signal Quality for Closed-Loop Neuro Stimulation via Source-Domain Denoising

2026-05-05

AI Summary

This study addresses the challenge posed by strong artifacts in transcranial magnetic stimulation (TMS)-evoked electroencephalography (EEG) signals, which hinder their application in closed-loop neuromodulation and brain–computer interfaces. The work presents the first standardized benchmark dataset for TMS-EEG denoising and systematically evaluates two mainstream source-domain denoising pipelines under conditions lacking ground-truth physiological signals, assessing both artifact suppression efficacy and preservation of genuine TMS-evoked responses. The proposed preprocessing framework demonstrates robust performance, significantly enhancing signal quality and establishing a unified benchmark for algorithm development. This advancement facilitates more reliable use of TMS-EEG in neuroscience research, clinical settings, and embedded brain–computer interface systems.

Abstract

This research addresses a validated TMS EEG cleaning pipeline and a corresponding benchmark dataset. It evaluates two widely used artifact removal pipelines. A reference dataset of carefully preprocessed EEG signals was established to support future algorithm development and enable systematic comparison of automated artifact removal strategies, despite the absence of a true physiological ground truth. The study evaluates the effectiveness of two widely used source based artifact removal approaches and examines their impact on signal quality improvement and preservation of TMS-evoked potentials. The results support the robustness of the proposed preprocessing workflow and demonstrate its potential for improving data reliability in both research and clinical applications. A key goal is integrating TMS EEG and embedding it within a larger BCI framework. Ultimately, these efforts aim to enhance understanding of cortical dynamics and expand the clinical and research applications of TMS EEG.

Problem

• TMS-EEG
• artifact removal
• signal quality
• TMS-evoked potentials
• closed-loop neurostimulation

Innovation

• TMS-EEG
• source-domain denoising
• artifact removal
• benchmark dataset
• closed-loop neurostimulation