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Attention Score in Context
| Title |
Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates
|
|---|---|
| Published in |
Journal of NeuroEngineering and Rehabilitation, November 2014
|
| DOI | 10.1186/1743-0003-11-153 |
| Pubmed ID | |
| Authors |
Eduardo López-Larraz, Luis Montesano, Ángel Gil-Agudo, Javier Minguez |
| Abstract |
Brain-machine interfaces (BMI) have recently been integrated within motor rehabilitation therapiesby actively involving the central nervous system (CNS) within the exercises. For instance, the onlinedecoding of intention of motion of a limb from pre-movement EEG correlates is being used to convertpassive rehabilitation strategies into active ones mediated by robotics. As early stages of upper limbmotor rehabilitation usually focus on analytic single-joint mobilizations, this paper investigates thefeasibility of building BMI decoders for these specific types of movements. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 50% |
| United States | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 201 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | <1% |
| Hungary | 1 | <1% |
| Taiwan | 1 | <1% |
| Mexico | 1 | <1% |
| Denmark | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 193 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 42 | 21% |
| Student > Master | 41 | 20% |
| Researcher | 19 | 9% |
| Student > Bachelor | 19 | 9% |
| Student > Doctoral Student | 16 | 8% |
| Other | 22 | 11% |
| Unknown | 42 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 62 | 31% |
| Neuroscience | 29 | 14% |
| Medicine and Dentistry | 13 | 6% |
| Computer Science | 12 | 6% |
| Nursing and Health Professions | 11 | 5% |
| Other | 24 | 12% |
| Unknown | 50 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 20 January 2017.
All research outputs
#7,047,316
of 25,373,627 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#408
of 1,413 outputs
Outputs of similar age
#71,233
of 268,531 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#8
of 19 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,413 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has gotten more attention than average, scoring higher than 70% 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 268,531 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 19 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 57% of its contemporaries.