Journal ArticleDOI
Ocular artifact elimination from electroencephalography signals: A systematic review
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TLDR
This paper attempts to give an extensive outline of the advancement in methodologies to eliminate one of the most common artifacts, i.e., ocular artifact, from EEG signal with a validated simulation model on the recorded EEG signal.About:
This article is published in Biocybernetics and Biomedical Engineering.The article was published on 2021-07-01. It has received 24 citations till now. The article focuses on the topics: Artifact (error) & Electroencephalography.read more
Citations
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Measurement of Pain-like Response to Various NICU Stimulants for High-risk Infants Using CRIES, FLACC and PIPP
TL;DR: In this article, the authors examined the pain-like responses to frequent stimulants in the neonatal intensive care unit (NICU) using CRIES, FLACC and PIPP, and the clinical feasibility and validity of using these pain measurements for high-risk infants.
Posted ContentDOI
Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and Application to Oscillations
N. Bailey,Mana Biabani,A. Hill,Aleksandra Miljevic,N. Rogasch,Brooke McQueen,O. Murphy,Pb. Fitzgerald +7 more
TL;DR: RelAX (the Reduction of Electroencephalographic Artifacts), an automated EEG cleaning pipeline implemented within EEGLAB that reduces all artifact types, is developed and recommended for data cleaning across EEG studies.
Posted ContentDOI
Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data – Part 2: Application to Event-Related Potentials
N. Bailey,A. Hill,Mana Biabani,O. Murphy,N. Rogasch,Brooke McQueen,Aleksandra Miljevic,Pb. Fitzgerald +7 more
TL;DR: This companion article introduced RELAX (the Reduction of Electroencephalographic Artifacts), an automated and modular cleaning pipeline that reduces artifacts with Multiple Wiener Filtering and wavelet enhanced independent component analysis ( wICA) applied to artifact components detected with ICLabel (wICA_ICLabel) (Bailey et al., 2022).
Journal ArticleDOI
Introducing RELAX: An automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and application to oscillations
N. Bailey,Mana Biabani,A. Hill,Aleksandra Miljevic,N. Rogasch,Brooke McQueen,O. Murphy,Pb. Fitzgerald +7 more
TL;DR: Relax as mentioned in this paper is a fully automated EEG cleaning pipeline that addresses all artifact types and improves measurement of EEG outcomes by using multi-channel Wiener filtering and wavelet enhanced independent component analysis (wICA_ICLabel).
Journal ArticleDOI
RELAX part 2: A fully automated EEG data cleaning algorithm that is applicable to Event-Related-Potentials
N. Bailey,A. Hill,Mana Biabani,O. Murphy,N. Rogasch,Brooke McQueen,Aleksandra Miljevic,Paul B. Fitzgerald +7 more
TL;DR: In this article , the RELAX (Reduction of Electroencephalographic Artifacts) pre-processing pipeline was used to clean EEG data for Event-Related Potentials (ERP) analysis.
References
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Journal ArticleDOI
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
Arnaud Delorme,Scott Makeig +1 more
TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.
Journal ArticleDOI
Independent component analysis: algorithms and applications
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.
Journal ArticleDOI
Removing electroencephalographic artifacts by blind source separation.
Tzyy-Ping Jung,Tzyy-Ping Jung,Scott Makeig,Colin Humphries,Te-Won Lee,Te-Won Lee,Martin J. McKeown,Vicente J. Iragui,Terrence J. Sejnowski,Terrence J. Sejnowski +9 more
TL;DR: The results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods.
Journal ArticleDOI
Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis
TL;DR: Simulations demonstrate that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data.
Fundamentals of eeg measurement
TL;DR: This review article presents an introduction into EEG measurement, a completely non-invasive procedure that can be applied repeatedly to patients, normal adults, and children with virtually no risk or limitation.