Evidence map›Paper›PMID 28217081›Full record

ArticleFrontiers in neuroscience2017

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

Kelly Kleifges, Nima Bigdely-Shamlo, Scott E Kerick, Kay A Robbins

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kelly KleifgesDepartment of Computer Science, University of Texas at San Antonio San Antonio, TX, USA.
Nima Bigdely-ShamloQusp Labs, Qusp San Diego, CA, USA.
Scott E KerickUS Army Research Laboratory Aberdeen, MD, USA.
Kay A RobbinsDepartment of Computer Science, University of Texas at San Antonio San Antonio, TX, USA.

Funding

A Human Electrophysiology, Associated Anatomic Data and Integrated Tool ResourceR01MH084819 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GRETHE, JEFFREY S., MAKEIG, SCOTT · 2009 to 2011
$1.0M
NIMH NIH HHS R01 MH084819
6 · The paper itself

Abstract

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/.

Indexed as

artifactbig datablink durationEEGEEGLABeye blinkshuman behaviormachine learning

Identifiers

PMID28217081
PMCPMC5289990

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.