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Mendeley readers
Attention Score in Context
| Title |
Non-linear Parameter Estimates from Non-stationary MEG Data
|
|---|---|
| Published in |
Frontiers in Neuroscience, August 2016
|
| DOI | 10.3389/fnins.2016.00366 |
| Pubmed ID | |
| Authors |
Juan D. Martínez-Vargas, Jose D. López, Adam Baker, German Castellanos-Dominguez, Mark W. Woolrich, Gareth Barnes |
| Abstract |
We demonstrate a method to estimate key electrophysiological parameters from resting state data. In this paper, we focus on the estimation of head-position parameters. The recovery of these parameters is especially challenging as they are non-linearly related to the measured field. In order to do this we use an empirical Bayesian scheme to estimate the cortical current distribution due to a range of laterally shifted head-models. We compare different methods of approaching this problem from the division of M/EEG data into stationary sections and performing separate source inversions, to explaining all of the M/EEG data with a single inversion. We demonstrate this through estimation of head position in both simulated and empirical resting state MEG data collected using a head-cast. |
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 % |
|---|---|---|
| Colombia | 1 | 50% |
| Switzerland | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 27% |
| Student > Ph. D. Student | 5 | 19% |
| Student > Master | 4 | 15% |
| Professor | 3 | 12% |
| Other | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 3 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 7 | 27% |
| Engineering | 5 | 19% |
| Agricultural and Biological Sciences | 2 | 8% |
| Physics and Astronomy | 2 | 8% |
| Psychology | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 7 | 27% |
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 07 September 2016.
All research outputs
#19,942,887
of 25,373,627 outputs
Outputs from Frontiers in Neuroscience
#8,668
of 11,538 outputs
Outputs of similar age
#262,779
of 355,231 outputs
Outputs of similar age from Frontiers in Neuroscience
#94
of 132 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% 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 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.