Improving TMS EEG Signal Quality for Closed-Loop Neuro Stimulation via Source-Domain Denoising
For researchers and clinicians using TMS EEG, this work provides a robust preprocessing workflow to enhance signal quality, though it is incremental as it evaluates existing methods on a new dataset.
This research establishes a validated TMS EEG cleaning pipeline and benchmark dataset, evaluating two source-based artifact removal approaches to improve signal quality and preserve TMS-evoked potentials, supporting data reliability for research and clinical applications.
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.