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

TL;DR

This paper presents a validated TMS EEG cleaning pipeline and benchmark dataset to improve artifact removal, signal quality, and reliability for research and clinical applications.

Contribution

It introduces a standardized preprocessing workflow and benchmark dataset for systematic comparison of artifact removal strategies in TMS EEG.

Findings

The proposed preprocessing workflow is robust and effective.

Artifact removal impacts TMS-evoked potential preservation.

The benchmark dataset supports future algorithm development.

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.