James A Desjardins1, Stefon van Noordt2, Scott Huberty3, Sidney J Segalowitz4, Mayada Elsabbagh5. 1. Azrieli Centre for Autism Research, Montreal Neurological Institute-Hospital, McGill University, Montréal, Canada; SHARCNET, Compute Ontario, Compute Canada, Canada. Electronic address: james.desjardins@computeontario.ca. 2. Azrieli Centre for Autism Research, Montreal Neurological Institute-Hospital, McGill University, Montréal, Canada. Electronic address: stefonv0@gmail.com. 3. Azrieli Centre for Autism Research, Montreal Neurological Institute-Hospital, McGill University, Montréal, Canada. Electronic address: scott.huberty@mail.mcgill.ca. 4. Cognitive and Affective Neuroscience Lab, Brock University, St. Catharines, ON, Canada. Electronic address: sid.segalowitz@brocku.ca. 5. Azrieli Centre for Autism Research, Montreal Neurological Institute-Hospital, McGill University, Montréal, Canada; Douglas Mental Health University Institute, Verdun, Canada. Electronic address: mayada.elsabbagh@mcgill.ca.
Abstract
BACKGROUND: The methods available for pre-processing EEG data are rapidly evolving as researchers gain access to vast computational resources; however, the field currently lacks a set of standardized approaches for data characterization, efficient interactive quality control review procedures, and large-scale automated processing that is compatible with High Performance Computing (HPC) resources. NEW METHOD: In this paper we describe an infrastructure for the development of standardized procedures for semi and fully automated pre-processing of EEG data. Our pipeline incorporates several methods to isolate cortical signal from noise, maintain maximal information from raw recordings and provide comprehensive quality control and data visualization. In addition, batch processing procedures are integrated to scale up analyses for processing hundreds or thousands of data sets using HPC clusters. RESULTS: We demonstrate here that by using the EEG Integrated Platform Lossless (EEG-IP-L) pipeline's signal quality annotations, significant increase in data retention is achieved when applying subsequent post-processing ERP segment rejection procedures. Further, we demonstrate that the increase in data retention does not attenuate the ERP signal. CONCLUSIONS: The EEG-IP-L state provides the infrastructure for an integrated platform that includes long-term data storage, minimal data manipulation and maximal signal retention, and flexibility in post processing strategies.
BACKGROUND: The methods available for pre-processing EEG data are rapidly evolving as researchers gain access to vast computational resources; however, the field currently lacks a set of standardized approaches for data characterization, efficient interactive quality control review procedures, and large-scale automated processing that is compatible with High Performance Computing (HPC) resources. NEW METHOD: In this paper we describe an infrastructure for the development of standardized procedures for semi and fully automated pre-processing of EEG data. Our pipeline incorporates several methods to isolate cortical signal from noise, maintain maximal information from raw recordings and provide comprehensive quality control and data visualization. In addition, batch processing procedures are integrated to scale up analyses for processing hundreds or thousands of data sets using HPC clusters. RESULTS: We demonstrate here that by using the EEG Integrated Platform Lossless (EEG-IP-L) pipeline's signal quality annotations, significant increase in data retention is achieved when applying subsequent post-processing ERP segment rejection procedures. Further, we demonstrate that the increase in data retention does not attenuate the ERP signal. CONCLUSIONS: The EEG-IP-L state provides the infrastructure for an integrated platform that includes long-term data storage, minimal data manipulation and maximal signal retention, and flexibility in post processing strategies.
Authors: Rianne Haartsen; Luke Mason; Eleanor K Braithwaite; Teresa Del Bianco; Mark H Johnson; Emily J H Jones Journal: Dev Psychobiol Date: 2021-11 Impact factor: 2.531
Authors: Anna Kaiser; Pascal-M Aggensteiner; Martin Holtmann; Andreas Fallgatter; Marcel Romanos; Karina Abenova; Barbara Alm; Katja Becker; Manfred Döpfner; Thomas Ethofer; Christine M Freitag; Julia Geissler; Johannes Hebebrand; Michael Huss; Thomas Jans; Lea Teresa Jendreizik; Johanna Ketter; Tanja Legenbauer; Alexandra Philipsen; Luise Poustka; Tobias Renner; Wolfgang Retz; Michael Rösler; Johannes Thome; Henrik Uebel-von Sandersleben; Elena von Wirth; Toivo Zinnow; Sarah Hohmann; Sabina Millenet; Nathalie E Holz; Tobias Banaschewski; Daniel Brandeis Journal: Brain Sci Date: 2021-02-10
Authors: Scott Huberty; Virginia Carter Leno; Stefon J R van Noordt; Rachael Bedford; Andrew Pickles; James A Desjardins; Sara Jane Webb; Mayada Elsabbagh Journal: Autism Res Date: 2021-05-06 Impact factor: 4.633