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A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy

Overview of attention for article published in Frontiers in Neuroinformatics, September 2022
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Mentioned by

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2 X users

Citations

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1 Dimensions

Readers on

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9 Mendeley
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Title
A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy
Published in
Frontiers in Neuroinformatics, September 2022
DOI 10.3389/fninf.2022.771965
Pubmed ID
Authors

Xiangyu Zhao, Xueping Peng, Ke Niu, Hailong Li, Lili He, Feng Yang, Ting Wu, Duo Chen, Qiusi Zhang, Menglin Ouyang, Jiayang Guo, Yijie Pan

X Demographics

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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 22%
Unspecified 1 11%
Unknown 6 67%
Readers by discipline Count As %
Nursing and Health Professions 1 11%
Agricultural and Biological Sciences 1 11%
Computer Science 1 11%
Unknown 6 67%
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 26 September 2022.
All research outputs
#18,896,869
of 23,414,653 outputs
Outputs from Frontiers in Neuroinformatics
#637
of 769 outputs
Outputs of similar age
#301,498
of 433,329 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#19
of 30 outputs
Altmetric has tracked 23,414,653 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 769 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one is in the 10th percentile – i.e., 10% 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 433,329 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.