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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.

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|December 25, 2025
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Summary

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

Keywords:
Artifacts removalConcurrent TMS-iEEGDetrendingFilteringRe-referencingSignal preprocessingTMS-evoked potentials

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Area of Science:

  • Neuroscience
  • Brain-Computer Interfaces
  • Signal Processing

Background:

  • Transcranial magnetic stimulation combined with intracranial EEG (TMS-iEEG) is crucial for understanding brain organization and dynamics.
  • TMS-induced artifacts in iEEG data present significant challenges for accurate neural response analysis.

Purpose of the Study:

  • To develop and validate a practical preprocessing pipeline for single-pulse TMS-iEEG data.
  • To systematically evaluate the impact of preprocessing steps on artifact reduction and subsequent analysis of TMS-evoked potentials (iTEPs).

Main Methods:

  • A preprocessing pipeline including re-referencing, filtering, artifact interpolation, and detrending was developed.
  • The pipeline's effectiveness was assessed using both real and simulated TMS-iEEG data.
  • Methodological choices within the pipeline were systematically compared.

Main Results:

  • The proposed pipeline effectively attenuated TMS-induced artifacts and noise, leading to cleaner iEEG signals.
  • Specific preprocessing choices, particularly referencing strategies, significantly influenced the morphology and amplitude of intracranial TMS-evoked potentials (iTEPs).
  • A segment-based filtering approach, excluding artifact windows, is recommended to minimize signal distortion.

Conclusions:

  • The developed preprocessing pipeline offers a practical solution for handling artifacts in TMS-iEEG data.
  • Tailoring preprocessing methods, especially referencing, to specific data characteristics is essential for accurate iTEP analysis.
  • This work contributes to establishing a standardized framework for TMS-iEEG research, advancing the understanding of brain function and TMS effects.