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Related Experiment Video

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The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for

Laurel J Gabard-Durnam1, Adriana S Mendez Leal1, Carol L Wilkinson1

  • 1Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, United States.

Frontiers in Neuroscience
|March 15, 2018
PubMed
Summary

The Harvard Automated Processing Pipeline for EEG (HAPPE) offers automated processing for challenging developmental electroencephalography (EEG) data. This tool effectively removes artifacts while preserving signal quality, outperforming existing methods.

Keywords:
EEGEEG processingartifact removalautomateddata qualitydevelopmentelectroencephalographypipeline

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Data Science

Background:

  • Electroencephalography (EEG) data from developmental populations present unique processing challenges, including high artifact levels and short recording durations.
  • Traditional manual artifact rejection methods are becoming unsustainable with increasing sample sizes and EEG channel density.
  • Lack of standardized automated tools and data quality metrics hinders reliable EEG analysis in vulnerable populations.

Purpose of the Study:

  • To introduce the Harvard Automated Processing Pipeline for EEG (HAPPE), a novel automated system for processing challenging EEG data.
  • To provide a standardized, automated solution for EEG data from developmental populations, accommodating variable artifact levels and recording lengths.
  • To establish consistent data quality reporting for EEG studies.

Main Methods:

  • HAPPE is a standardized, automated pipeline designed for raw EEG data processing, including filtering, artifact rejection, and re-referencing.
  • The pipeline is compatible with both event-related and resting-state EEG data, producing outputs suitable for time-frequency analyses.
  • A post-processing report detailing data quality metrics is generated to ensure standardized evaluation.

Main Results:

  • HAPPE demonstrated superior performance compared to seven alternative processing approaches in an example analysis.
  • The pipeline effectively removed more artifacts than all alternative methods while preserving equivalent or greater EEG signal.
  • Reference distributions for HAPPE's data quality metrics were provided using an 867-file dataset, demonstrating robustness across varied data.

Conclusions:

  • HAPPE offers a robust, automated solution for processing complex EEG data from developmental populations.
  • The pipeline enhances data quality assessment and reporting, crucial for studies involving high artifact contamination or short recordings.
  • HAPPE is freely available, promoting standardized and reproducible EEG research in neuroscience and related fields.