Automagic: Standardized preprocessing of big EEG data

  title={Automagic: Standardized preprocessing of big EEG data},
  author={Andreas Pedroni and Amir Bahreini and Nicolas Langer},
Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exist a variety of standardized preprocessing methods to clean… 
HAPPILEE: The Harvard Automated Processing Pipeline In Low Electrode Electroencephalography, a standardized software for low density EEG and ERP data
The HAPPILEE pipeline is proposed as a standardized, automated pipeline optimized for EEG recordings with low density channel layouts of any size and includes post-processing reports of data and pipeline quality metrics to facilitate the evaluation and reporting of data quality and processing-related changes to the data in a standardized manner.
Automated Pipeline for Infants Continuous EEG (APICE): a flexible pipeline for developmental studies
An Automated Pipeline for Infants Continuous EEG (APICE), which is fully automated, flexible, and modular, and tested the combination of APICE with common data cleaning methods such as Independent Component Analysis and Denoising Source Separation.
EPOS: EEG Processing Open-Source Scripts
A tutorial-like EEG (pre-)processing pipeline to achieve an automated method based on the semi-automated analysis proposed by Delorme and Makeig, and is compared with a selection of existing approaches.
Decomposing age effects in EEG alpha power
It is hypothesized that previously reported age-related alpha power differences will disappear when absolute power is adjusted for the a periodic signal component, and the test–retest reliability of the aperiodic and aperiodics-adjusted signal components will be assessed.
Removal of eye-blinking artifacts by ICA in cross-modal long-term EEG recording
It was found that, firstly, down sampling is an effective way to reduce the computation time in ICA, and dimension reduction by PCA was also a way to improve the efficiency and effectiveness of ICA.
EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)
It was found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors, and it was showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity.
Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks: a simulation study
Evaluating some of the parameters related to the EEG source connectivity analysis showed that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks, and showed a significant variability in the performance of the tested inverse solutions and connectivity measures.
A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE)
The ALICE toolbox aims to build a sustainable algorithm not only to remove artifacts but also to find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge, and implements the novel strategy for consentient labeling of ICs by several experts.
HD-EEG for tracking sub-second brain dynamics during cognitive tasks
High-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms are provided to validate several parameters involved in the methods used to estimate cortical brain networks through scalp EEG and to better understand the general functioning of the brain during rest and task.