Dataset Information

Automated Pipeline for Infants Continuous EEG (APICE): A flexible pipeline for developmental cognitive studies.

ABSTRACT: Infant electroencephalography (EEG) presents several challenges compared with adult data: recordings are typically short and heavily contaminated by motion artifacts, and the signal changes throughout development. Traditional data preprocessing pipelines, developed mainly for event-related potential analyses, require manual steps. However, larger datasets make this strategy infeasible. Moreover, new analytical approaches may have different preprocessing requirements. We propose an Automated Pipeline for Infants Continuous EEG (APICE). APICE is fully automated, flexible, and modular. The use of multiple algorithms and adaptive thresholds for artifact detection makes it suitable across age groups and testing procedures. Furthermore, the preprocessing is performed on continuous data, enabling better data recovery and flexibility (i.e., the same preprocessing is usable for different analyzes). Here we describe APICE and validate its performance in terms of data quality and data recovery using two very different infant datasets. Specifically, (1) we show how APICE performs when varying its artifacts rejection sensitivity; (2) we test the effect of different data cleaning methods such as the correction of transient artifacts, Independent Component Analysis, and Denoising Source Separation; and (3) we compare APICE with other available pipelines. APICE uses EEGLAB and compatible custom functions. It is freely available at https://github.com/neurokidslab/eeg_preprocessing, together with example scripts.

SUBMITTER: Flo A

PROVIDER: S-EPMC8804179 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Automated Pipeline for Infants Continuous EEG (APICE): A flexible pipeline for developmental cognitive studies.

Fló Ana A Gennari Giulia G Benjamin Lucas L Dehaene-Lambertz Ghislaine G

Developmental cognitive neuroscience 20220125

Infant electroencephalography (EEG) presents several challenges compared with adult data: recordings are typically short and heavily contaminated by motion artifacts, and the signal changes throughout development. Traditional data preprocessing pipelines, developed mainly for event-related potential analyses, require manual steps. However, larger datasets make this strategy infeasible. Moreover, new analytical approaches may have different preprocessing requirements. We propose an Automated Pipe ...[more]

PMID: 35093730

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Project description:Objective.Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR).Approach.The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs);n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database.Main results.We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data.Significance.APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters' preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.

Dataset Information

Automated Pipeline for Infants Continuous EEG (APICE): A flexible pipeline for developmental cognitive studies.

Publications

Automated Pipeline for Infants Continuous EEG (APICE): A flexible pipeline for developmental cognitive studies.

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