提出去噪流程提升经颅磁刺激脑电信号质量，助力闭环神经调控。
Improving TMS EEG Signal Quality for Closed-Loop Neuro Stimulation via Source-Domain Denoising
- 基于源域分析的去噪方法，有效分离伪迹与真实信号。
- 显著提升TMS诱发脑电位的信噪比，保留关键神经响应特征。
- 为脑机接口和临床研究提供可靠数据基础，适合神经工程领域应用。
本研究提出了一套经过验证的经颅磁刺激-脑电（TMS EEG）信号清洗流程，并建立了相应的基准数据集。评估了两种广泛使用的基于源的伪迹去除方法，通过精心预处理的参考数据集支持未来算法开发，并实现对自动伪迹去除策略的系统性比较。尽管缺乏真实的生理真值，研究仍验证了所提预处理流程的稳健性，展示了其在改善信号质量、保留TMS诱发电位方面的潜力，有助于提升科研与临床应用中的数据可靠性。核心目标是将TMS EEG整合进更大的脑机接口框架中，最终增进对皮层动态的理解，拓展其在临床与研究中的应用前景。
原文摘要 · Abstract (English)
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.
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