NEAR: An artifact removal pipeline for human newborn EEG data
| Autor: | Velu Prabhakar Kumaravel, Elisabetta Farella, Eugenio Parise, Marco Buiatti |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Neurophysiology and neuropsychology
Adult EEG Newborns Infants Artifact Removal EEGLAB Artifact Subspace Reconstruction genetic structures Artifact removal Cognitive Neuroscience QP351-495 Movement Infant Newborn Infant Electroencephalography Signal Processing Computer-Assisted Local Outlier Factor Humans Articles from the Special Issue on EEG Methods for Developmental Cognitive Neuroscientists: A Tutorial Approach Edited by George Buzzell Emilio Valadez Santiago Morales Nathan Fox Sabine Hunnius Artifacts Algorithms |
| Zdroj: | Developmental Cognitive Neuroscience Developmental Cognitive Neuroscience, Vol 54, Iss, Pp 101068-(2022) |
| Popis: | Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging. Compared to adults and older infants, datasets are typically much shorter due to newborns’ limited attentional span and much noisier due to non-stereotyped artifacts mainly caused by uncontrollable movements. We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns. NEAR is based on two key steps: 1) A novel bad channel detection tool based on the Local Outlier Factor (LOF), a robust outlier detection algorithm; 2) A parameter calibration procedure for adapting to newborn EEG data the algorithm Artifacts Subspace Reconstruction (ASR), developed for artifact removal in mobile adult EEG. Tests on simulated data showed that NEAR outperforms existing methods in removing representative newborn non-stereotypical artifacts. NEAR was validated on two developmental populations (newborns and 9-month-old infants) recorded with two different experimental designs (frequency-tagging and ERP). Results show that NEAR artifact removal successfully reproduces established EEG responses from noisy datasets, with a higher statistical significance than the one obtained by existing artifact removal methods. The EEGLAB-based NEAR pipeline is freely available at https://github.com/vpKumaravel/NEAR. |
| Databáze: | OpenAIRE |
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