Automated Pipeline for Infants Continuous EEG (APICE): a flexible pipeline for developmental studies

Abstract

Infant electroencephalography (EEG) presents several challenges compared with adult data. Recordings are typically short. Motion artifacts heavily contaminate the data. The EEG neural signal and the artifacts change throughout development. Traditional data preprocessing pipelines have been developed mainly for event-related potentials analyses, and they required manual steps, or use fixed thresholds for rejecting epochs. However, larger datasets make the use of manual steps infeasible, and new analytical approaches may have different preprocessing requirements. Here we propose an Automated Pipeline for Infants Continuous EEG (APICE). APICE is fully automated, flexible, and modular. Artifacts are detected using multiple algorithms and adaptive thresholds, making it suitable to different age groups and testing procedures without redefining parameters. Artifacts detection and correction of transient artifacts is performed on continuous data, allowing for better data recovery and providing flexibility (i.e., the same preprocessing is usable for different analyses). Here we describe APICE and validate it using two infant datasets of different ages tested in different experimental paradigms. We also tested the combination of APICE with common data cleaning methods such as Independent Component Analysis and Denoising Source Separation. APICE uses EEGLAB and compatible custom functions. It is freely available at https://github.com/neurokidslab/eeg_preprocessing, together with example scripts.

Brains in Sync: Practical Guideline for Parent–Infant EEG During Natural Interaction

“…The HAPPE and HAPPE + ER preprocessing pipelines, for instance, are great examples of MATLAB-based algorithms to clean and process continuous and event-related infant EEG data (Harvard Automated Processing Pipeline for Electroencephalography; Gabard-Durnam et al, 2018 ; HAPPE plus Event-Related; see preprint Monachino et al, 2021 ). Other recently developed EEG preprocessing pipelines include: the MADE preprocessing pipeline (Maryland Analysis for Developmental EEG; Debnath et al, 2020 ), the EEG-PI-L preprocessing pipeline (EEG Integrated Platform Lossless; Desjardins et al, 2021 ), the APICE (Automated Pipeline for Infants Continuous EEG, see preprint Fló et al, 2021 ), the ADJUST ICA algorithms ( Leach et al, 2020 ), and iMARA ICA algorithms ( Haresign et al, 2021b ). More details about the preprocessing steps can be found in the next paragraph.…”

Challenges and new perspectives of developmental cognitive EEG studies

“…Third, Fló et al (2022b) have recently developed the "Automated Pipeline for Infants Continuous EEG " (APICE toolbox; https://github. com/neurokidslab/eeg _ preprocessing ), in which automatized artifact detection is performed on the continuous data before further preprocessing.…”

From computing transition probabilities to word recognition in sleeping neonates, a two-step neural tale

“…Data were band-pass filter 0.1-40 Hz and pre-processed using custom MATLAB scripts based on the EEGLAB toolbox 2021.0 (Delorme & Makeig, 2004), according to the APICE pre-processing pipeline (Fló et al, 2021). The following steps were applied: (1) Data was band-pass filter 0.1-40 Hz.…”