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Updated: Jun 28, 2026

PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
06:51

PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing

Published on: June 6, 2025

Infant EEG preprocessing pipelines: A capability framework and current gaps in practice.

Efthymios Papatzikis1

  • 1School of Health Sciences and Psychology, Canadian University Dubai, United Arab Emirates; "Panagiotis and Aglaia Kyriakou" Children's Hospital, Athens, Greece.

Journal of Neuroscience Methods
|June 26, 2026
PubMed
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A new framework evaluates infant electroencephalography (EEG) preprocessing tools for brain development research. No single tool excels in all areas, highlighting gaps in developmental calibration and validation.

Area of Science:

  • Neuroscience
  • Developmental Neuroscience
  • Biomedical Engineering

Background:

  • Infant electroencephalography (EEG) is crucial for studying early brain development.
  • Challenges in infant EEG include movement artifacts, low signal-to-noise ratio, and developmental changes.
  • Existing preprocessing tools lack a structured comparative evaluation.

Purpose of the Study:

  • To introduce a capability framework for evaluating infant EEG preprocessing tools.
  • To assess ten prominent infant EEG preprocessing pipelines across five key dimensions.
  • To guide researchers in selecting appropriate preprocessing methods for infant EEG data.

Main Methods:

  • Developed a capability framework with five dimensions: artifact handling, automation, flexibility, developmental sensitivity, and validation.
Keywords:
Capability FrameworkEEG Preprocessing and Processing PipelinesElectroencephalography AnalysisInfant EEGMaturity Levels

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Last Updated: Jun 28, 2026

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Published on: June 6, 2025

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  • Evaluated ten infant EEG preprocessing tools (nine infant-specific, one general-purpose) using the framework.
  • Assigned maturity levels (1-5) to each pipeline based on predefined descriptors, with two raters assessing agreement (Krippendorff's α =.69).

    • Main Results:

    • No single infant EEG preprocessing pipeline achieved top maturity across all five dimensions.
    • Each pipeline demonstrated a unique profile, suiting specific research needs.
    • A significant gap was identified in developmental sensitivity and cross-dataset validation across all evaluated pipelines.

    Conclusions:

    • The proposed capability framework offers a structured approach for informed preprocessing decisions in infant EEG research.
    • The findings reveal a field-wide need for improved developmental calibration and validation of infant EEG preprocessing tools.
    • Researchers and clinicians can utilize this framework to navigate the complexities of infant EEG data preprocessing effectively.