How to Process Infant Electroencephalography (Eeg) Data: A Critical Review of 10 Preprocessing Pipelines on Challenges, Strengths, and Future Directions

Infant electroencephalography (EEG) is an essential tool for understanding early brain development, yet it presents unique challenges such as frequent movement artifacts, low signal-to-noise ratio (SNR), and rapid developmental changes. In recent years, specialized preprocessing pipelines have been developed to address these issues and improve the accuracy of infant EEG data analysis. In alignment with this evolving research landscape, this critical review performs a comparative analysis of ten prominent preprocessing pipelines – APICE, ADJUST, GADS, BEAPP, HAPPE+ER, HAPPE, HAPPILEE, MADE, NEAR, and the Modular pipeline – focusing on five key criteria: artifact handling, automation and scalability, flexibility and adaptability, developmental sensitivity, and validation against empirical data. The review employs a multi-dimensional analysis framework developed using a reverse design approach, where the analysis criteria emerged organically through an in-depth study of the pipelines’ specific methods and applications. The comparative analysis involved benchmarking each pipeline against the five criteria using a structured system of qualitative descriptors translated into numerical indices, enabling in the end a more robust comparative overview of their strengths and limitations. Results of the critical review reveal that specific pipelines excel in automation and flexibility, making them suitable for large-scale or multi-site studies, while others demonstrate exceptional developmental sensitivity, specifically addressing the needs of neonatal EEG data. Overall, no single pipeline was found to be universally superior, but each contributes uniquely depending on specific research requirements. Future directions include enhancing automation, improving developmental sensitivity, and promoting empirical validation to ensure reproducibility and adaptability across diverse datasets. This critical analysis and review provides a foundation for researchers and clinicians to make informed decisions about preprocessing strategies in infant EEG studies, advancing the field of developmental neuroscience.