Improving TMS EEG Signal Quality for Closed-Loop Neuro Stimulation via Source-Domain Denoising
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917474206744576 |
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| author | Tang, Zhen Hamoodi, Ameer Foglia, Stevie Nelson, Aimee Gao, Zhen |
| author_facet | Tang, Zhen Hamoodi, Ameer Foglia, Stevie Nelson, Aimee Gao, Zhen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08184 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improving TMS EEG Signal Quality for Closed-Loop Neuro Stimulation via Source-Domain Denoising Tang, Zhen Hamoodi, Ameer Foglia, Stevie Nelson, Aimee Gao, Zhen Signal Processing Artificial Intelligence 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. |
| title | Improving TMS EEG Signal Quality for Closed-Loop Neuro Stimulation via Source-Domain Denoising |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2605.08184 |