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Mendeley readers
Attention Score in Context
| Title |
Reconstructing for joint angles on the shoulder and elbow from non-invasive electroencephalographic signals through electromyography
|
|---|---|
| Published in |
Frontiers in Neuroscience, January 2013
|
| DOI | 10.3389/fnins.2013.00190 |
| Pubmed ID | |
| Authors |
Kyuwan Choi |
| Abstract |
In this study, first the cortical activities over 2240 vertexes on the brain were estimated from 64 channels electroencephalography (EEG) signals using the Hierarchical Bayesian estimation while 5 subjects did continuous arm reaching movements. From the estimated cortical activities, a sparse linear regression method selected only useful features in reconstructing the electromyography (EMG) signals and estimated the EMG signals of 9 arm muscles. Then, a modular artificial neural network was used to estimate four joint angles from the estimated EMG signals of 9 muscles: one for movement control and the other for posture control. The estimated joint angles using this method have the correlation coefficient (CC) of 0.807 (±0.10) and the normalized root-mean-square error (nRMSE) of 0.176 (±0.29) with the actual joint angles. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 2% |
| United Kingdom | 1 | 2% |
| Germany | 1 | 2% |
| France | 1 | 2% |
| Unknown | 42 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 33% |
| Student > Master | 6 | 13% |
| Student > Doctoral Student | 4 | 9% |
| Researcher | 4 | 9% |
| Student > Bachelor | 2 | 4% |
| Other | 6 | 13% |
| Unknown | 9 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 17 | 37% |
| Computer Science | 6 | 13% |
| Medicine and Dentistry | 5 | 11% |
| Neuroscience | 5 | 11% |
| Nursing and Health Professions | 2 | 4% |
| Other | 1 | 2% |
| Unknown | 10 | 22% |
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 24 October 2013.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from Frontiers in Neuroscience
#10,135
of 11,538 outputs
Outputs of similar age
#258,406
of 288,986 outputs
Outputs of similar age from Frontiers in Neuroscience
#208
of 246 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,538 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 1st percentile – i.e., 1% 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 288,986 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 246 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.