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A Practical Preprocessing Pipeline for Concurrent TMS-iEEG: Critical Steps and Methodological Considerations.

Zhuoran Li1, Xianqing Liu1, Joshua Tatz2

  • 1University of Iowa Stead Family Department of Pediatrics, Iowa City, IA, USA.

Biorxiv : the Preprint Server for Biology
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a preprocessing pipeline for transcranial magnetic stimulation combined with intracranial EEG (TMS-iEEG) data. The pipeline effectively reduces TMS artifacts, improving the analysis of brain activity and TMS mechanisms.

Keywords:
Concurrent TMS-iEEGTMS-evoked potentialsartifacts removaldetrendingfilteringre-referencingsignal preprocessing

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Transcranial magnetic stimulation combined with intracranial EEG (TMS-iEEG) is vital for understanding brain function.
  • TMS-induced artifacts complicate the analysis of neural responses in TMS-iEEG data.

Purpose of the Study:

  • To develop and evaluate a practical preprocessing pipeline for single-pulse TMS-iEEG data.
  • To assess the impact of different preprocessing steps on artifact reduction and signal quality.
  • To provide guidelines for optimizing TMS-iEEG data analysis.

Main Methods:

  • Developed a preprocessing pipeline including re-referencing, filtering, artifact interpolation, and detrending.
  • Utilized both real and simulated TMS-iEEG data for systematic evaluation.
  • Compared alternative methodological choices within the pipeline.

Main Results:

  • The proposed pipeline effectively reduced TMS-induced artifacts and noise.
  • Cleaner intracranial TMS-evoked potentials (iTEPs) were obtained for subsequent analysis.
  • Methodological choices, particularly referencing, significantly influenced iTEP outcomes.

Conclusions:

  • The developed pipeline enhances the reliability of TMS-iEEG data analysis.
  • Tailoring referencing strategies is crucial for accurate iTEP interpretation.
  • Recommends segment-based filtering to mitigate artifact distortion.
  • Establishes a framework to advance concurrent TMS-iEEG research and understanding of brain organization.