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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Decoding methods for neural prostheses: where have we reached?
|
|---|---|
| Published in |
Frontiers in Systems Neuroscience, July 2014
|
| DOI | 10.3389/fnsys.2014.00129 |
| Pubmed ID | |
| Authors |
Zheng Li |
| Abstract |
This article reviews advances in decoding methods for brain-machine interfaces (BMIs). Recent work has focused on practical considerations for future clinical deployment of prosthetics. This review is organized by open questions in the field such as what variables to decode, how to design neural tuning models, which neurons to select, how to design models of desired actions, how to learn decoder parameters during prosthetic operation, and how to adapt to changes in neural signals and neural tuning. The concluding discussion highlights the need to design and test decoders within the context of their expected use and the need to answer the question of how much control accuracy is good enough for a prosthetic. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 3% |
| France | 2 | 2% |
| Germany | 1 | <1% |
| Unknown | 113 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 30 | 25% |
| Researcher | 17 | 14% |
| Student > Bachelor | 15 | 13% |
| Student > Master | 14 | 12% |
| Student > Doctoral Student | 8 | 7% |
| Other | 20 | 17% |
| Unknown | 16 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 48 | 40% |
| Neuroscience | 20 | 17% |
| Agricultural and Biological Sciences | 15 | 13% |
| Computer Science | 5 | 4% |
| Psychology | 4 | 3% |
| Other | 5 | 4% |
| Unknown | 23 | 19% |
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 16 September 2016.
All research outputs
#17,722,431
of 22,757,541 outputs
Outputs from Frontiers in Systems Neuroscience
#1,053
of 1,340 outputs
Outputs of similar age
#153,872
of 226,888 outputs
Outputs of similar age from Frontiers in Systems Neuroscience
#37
of 42 outputs
Altmetric has tracked 22,757,541 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 1,340 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one is in the 15th percentile – i.e., 15% 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 226,888 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.