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X Demographics
Mendeley readers
Attention Score in Context
| Title |
An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks
|
|---|---|
| Published in |
Frontiers in Neuroscience, September 2020
|
| DOI | 10.3389/fnins.2020.00808 |
| Pubmed ID | |
| Authors |
Jing-Shan Huang, Yang Li, Bin-Qiang Chen, Chuang Lin, Bin Yao |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 25% |
| Switzerland | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 59 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 25% |
| Student > Master | 6 | 10% |
| Researcher | 3 | 5% |
| Student > Bachelor | 3 | 5% |
| Lecturer | 2 | 3% |
| Other | 3 | 5% |
| Unknown | 27 | 46% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 9 | 15% |
| Neuroscience | 7 | 12% |
| Computer Science | 5 | 8% |
| Medicine and Dentistry | 2 | 3% |
| Agricultural and Biological Sciences | 1 | 2% |
| Other | 4 | 7% |
| Unknown | 31 | 53% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 15 October 2020.
All research outputs
#3,050,984
of 25,387,668 outputs
Outputs from Frontiers in Neuroscience
#2,065
of 11,543 outputs
Outputs of similar age
#76,938
of 431,879 outputs
Outputs of similar age from Frontiers in Neuroscience
#145
of 324 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,543 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done well, scoring higher than 82% of its peers.
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 431,879 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 324 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.