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

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          Abstract

          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.

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              ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features.

              Abstract A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                27 February 2018
                2018
                : 12
                : 97
                Affiliations
                [1] 1Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Harvard University , Boston, MA, United States
                [2] 2Department of Neurology, Boston Children's Hospital , Boston, MA, United States
                Author notes

                Edited by: Arnaud Delorme, UMR5549 Centre de Recherche Cerveau et Cognition (CerCo), France

                Reviewed by: Fabrizio De Carli, Istituto di Bioimmagini e Fisiologia Molecolare (CNR), Italy; Andrey R. Nikolaev, KU Leuven, Belgium; Camillo Porcaro, Istituto di Scienze e Tecnologie della Cognizione (ISTC) - CNR, Italy; Andras Eke, Semmelweis University, Hungary

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2018.00097
                5835235
                29535597
                db8c1f69-1d05-4b19-b0a7-5b90e308f929
                Copyright © 2018 Gabard-Durnam, Mendez Leal, Wilkinson and Levin.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 October 2017
                : 06 February 2018
                Page count
                Figures: 10, Tables: 5, Equations: 1, References: 43, Pages: 24, Words: 14053
                Categories
                Neuroscience
                Technology Report

                Neurosciences
                eeg,electroencephalography,automated,pipeline,artifact removal,data quality,eeg processing,development

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