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

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:

• 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.