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Preprocessing on the Go: Practices in Gait-Related Mobile EEG.

Vaishali Vinod1, Lara Johanna Papin2, Robbin Romijnders1

  • 1Department of Neurology, University Hospital Schleswig-Holstein Campus Kiel and Kiel University, Kiel, Germany.

Psychophysiology
|June 25, 2026
PubMed
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Mobile electroencephalography (EEG) studies on gait show varied data processing methods. This review maps these diverse preprocessing pipelines, highlighting inconsistencies in artifact rejection and reporting, which hinders cross-study comparisons.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Gait Analysis

Background:

  • Mobile electroencephalography (EEG) is increasingly used to study brain activity during human locomotion.
  • The refinement of preprocessing pipelines for mobile EEG data is crucial for accurate analysis of gait dynamics.
  • Existing preprocessing methods exhibit significant diversity, complicating the comparison of findings across different research studies.

Purpose of the Study:

  • To systematically review and map the preprocessing pipelines employed in studies combining mobile EEG and gait measurements.
  • To identify and visualize the heterogeneity in preprocessing steps, their order, combinations, and reporting detail.
  • To highlight variations in artifact rejection methods and their impact on data comparability.

Main Methods:

Keywords:
artifact rejectiongaitmobile EEGpreprocessingwalking

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  • Conducted a comprehensive literature review of studies integrating mobile EEG with gait analysis.
  • Identified and categorized preprocessing pipeline components, from raw data to derived outcomes like PSD, ERSP, ERD/ERS, and CMC.
  • Visualized the diversity of pipelines and analyzed variations, particularly in artifact rejection techniques and reporting standards.

Main Results:

  • Substantial heterogeneity was observed in preprocessing pipeline steps, their sequence, and combinations across studies.
  • Significant variability exists in the tools and reporting practices for artifact rejection.
  • The diverse approaches challenge the comparability of results derived from different mobile EEG and gait studies.

Conclusions:

  • The current landscape of mobile EEG preprocessing for gait analysis is characterized by considerable heterogeneity.
  • Inconsistent artifact rejection and reporting practices impede cross-study comparisons and the establishment of field-wide standards.
  • There is a critical need for transparent reporting standards and the development of shared methodologies within the mobile EEG research community.