The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data

By L. J. Gabard-Durnam, Adriana S Méndez Leal, C. Wilkinson, A. Levin. Frontiers in Neuroscience, 2018

470 citations recorded by the source corpus.

Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/25aaf101ca68052a2caebadc7b3073b5d904e279

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

C. Wilkinson

In Cognitive Neuroscience

In Signal Processing

Citations in the corpus

Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings (2020, unknown): “[30], and Ojeda et al.”
A human electrophysiological signature of Fragile X pathophysiology is shared in V1 of Fmr1-/y mice (2026, unknown): “Brie ﬂ y, data were 1 Hz high-pass and 100 Hz low-pass ﬁ ltered, down-sampled to 250Hz, and then run through the HAPPE module for 60 Hz line noise removal, bad channel rejection and artifact removal using combined wavelet-enhanced independent component analysis (ICA) and Multiple Artifact Rejection ”
Resting state aperiodic and periodic EEG activity in preschool-aged autistic children: differences from neurotypical peers and links to language skills (2026, unknown): “The resting-state EEG raw NetStation (Electrical Geo-desics Inc.) files were exported to MATLAB (version R2022a) for preprocessing using the Batch Electroencephalography Automated Processing Platform (BEAPP [49]) with the integrated Harvard Automated Prepro-cessing Pipeline for EEG (HAPPE [50]).”
Longitudinal trajectories of aperiodic EEG activity in early to middle childhood (2024, methodology): “HAPPE’s amplitude and joint probability criteria were used to reject epochs contaminated with artifact.”
Alterations in aperiodic and periodic EEG activity in young children with Down syndrome (2024, unknown): “…(Electrical Geodesics, Inc) files were exported to MATLAB (MathWorks) for pre-processing and power analysis using the Batch EEG Automated Progressing Platform (BEAPP (Levin et al., 2018)) with integrated Harvard Automated Preprocessing Pipeline for EEG (HAPPE (Gabard-Durnam et al., 2018)).”
RELAX‐Jr: An Automated Pre‐Processing Pipeline for Developmental EEG Recordings (2024, unknown): “In contrast, the worst performers (in terms of mean values) in the eyes open data were MADE, ASR and HAPPE, with ASR and HAPPE pipelines rejecting 100% of epochs for some files, and ASR rejecting more than 25% of epochs on average.”
Evaluating early EEG correlates of restricted and repetitive behaviors for toddlers with or without autism (2024, methodology): “Data preprocessing, artifact removal, and data quality assessment were carried out via the Harvard Automated Processing Pipeline for EEG (HAPPE 1.0; [51], a preprocessing pipeline optimized for developmental EEG data).”
The developmental trajectory of functional excitation‐inhibition balance relates to language abilities in autistic and allistic children (2023, methodology): “Preprocessing was done automatically using an adjusted version of the Harvard Automated Preprocessing Pipeline (HAPPE; Gabard-Durnam et al., 2018; Menn, Michel, et al., 2022).”
Maximizing the potential of EEG as a developmental neuroscience tool (2023, methodology): “When applied to developmental EEG data, these pipelines have been shown to yield increased trial retention, compared to other pipelines designed for adults or using a traditional approach of simply conducting artifact rejection (Debnath et al., 2020, 2020; Gabard-Durnam et al., 2018).”
Theta power reduction and theta–gamma coupling desynchronization are associated with working memory interference and anxiety symptoms in panic disorder: a retrospective study (2024, unknown): “Channels with error probabilities exceeding three standard deviations from the mean were deemed “bad chan-nels” and removed [61].”
Monitoring pilot trainees’ cognitive control under a simulator-based training process with EEG microstate analysis (2024, unknown): “Moreover, the signals were segmented in 2-second epochs for detecting bad segments and bad local channels within each segment 53 .”
The Functional Interactions between Cortical Regions through Theta-Gamma Coupling during Resting-State and a Visual Working Memory Task (2022, methodology): “The Harvard Automated Processing Pipeline for EEG [87] was used to automate preprocessing and artifact correction using Wavelet-enhanced independent component analysis (ICA) and the Multiple Artifact Rejection Algorithm (MARA) [88].”