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BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis
Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and ta...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5289990/ https://www.ncbi.nlm.nih.gov/pubmed/28217081 http://dx.doi.org/10.3389/fnins.2017.00012 |
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author | Kleifges, Kelly Bigdely-Shamlo, Nima Kerick, Scott E. Robbins, Kay A. |
author_facet | Kleifges, Kelly Bigdely-Shamlo, Nima Kerick, Scott E. Robbins, Kay A. |
author_sort | Kleifges, Kelly |
collection | PubMed |
description | Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/. |
format | Online Article Text |
id | pubmed-5289990 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-52899902017-02-17 BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis Kleifges, Kelly Bigdely-Shamlo, Nima Kerick, Scott E. Robbins, Kay A. Front Neurosci Neuroscience Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/. Frontiers Media S.A. 2017-02-03 /pmc/articles/PMC5289990/ /pubmed/28217081 http://dx.doi.org/10.3389/fnins.2017.00012 Text en Copyright © 2017 Kleifges, Bigdely-Shamlo, Kerick and Robbins. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Kleifges, Kelly Bigdely-Shamlo, Nima Kerick, Scott E. Robbins, Kay A. BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis |
title | BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis |
title_full | BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis |
title_fullStr | BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis |
title_full_unstemmed | BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis |
title_short | BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis |
title_sort | blinker: automated extraction of ocular indices from eeg enabling large-scale analysis |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5289990/ https://www.ncbi.nlm.nih.gov/pubmed/28217081 http://dx.doi.org/10.3389/fnins.2017.00012 |
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