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A Review of Resting-State Electroencephalography Analysis in Disorders of Consciousness

Overview of attention for article published in Frontiers in Neurology, September 2017
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Title
A Review of Resting-State Electroencephalography Analysis in Disorders of Consciousness
Published in
Frontiers in Neurology, September 2017
DOI 10.3389/fneur.2017.00471
Pubmed ID
Authors

Yang Bai, Xiaoyu Xia, Xiaoli Li

Abstract

Recently, neuroimaging technologies have been developed as important methods for assessing the brain condition of patients with disorders of consciousness (DOC). Among these technologies, resting-state electroencephalography (EEG) recording and analysis has been widely applied by clinicians due to its relatively low cost and convenience. EEG reflects the electrical activity of the underlying neurons, and it contains information regarding neuronal population oscillations, the information flow pathway, and neural activity networks. Some features derived from EEG signal processing methods have been proposed to describe the electrical features of the brain with DOC. The computation of these features is challenging for clinicians working to comprehend the corresponding physiological meanings and then to put them into clinical applications. This paper reviews studies that analyze spontaneous EEG of DOC, with the purpose of diagnosis, prognosis, and evaluation of brain interventions. It is expected that this review will promote our understanding of the EEG characteristics in DOC.

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 161 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 161 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 19%
Student > Master 22 14%
Researcher 21 13%
Student > Bachelor 19 12%
Other 8 5%
Other 24 15%
Unknown 36 22%
Readers by discipline Count As %
Neuroscience 39 24%
Medicine and Dentistry 25 16%
Engineering 19 12%
Psychology 14 9%
Agricultural and Biological Sciences 7 4%
Other 9 6%
Unknown 48 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 June 2018.
All research outputs
#18,349,015
of 23,577,761 outputs
Outputs from Frontiers in Neurology
#7,411
of 12,525 outputs
Outputs of similar age
#228,481
of 317,161 outputs
Outputs of similar age from Frontiers in Neurology
#112
of 193 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,525 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 317,161 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 193 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.