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X Demographics
Mendeley readers
Attention Score in Context
| Title |
From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM)
|
|---|---|
| Published in |
Frontiers in Neuroscience, July 2018
|
| DOI | 10.3389/fnins.2018.00368 |
| Pubmed ID | |
| Authors |
Johannes J. Fahrenfort, Joram van Driel, Simon van Gaal, Christian N. L. Olivers |
| Abstract |
In recent years, time-resolved multivariate pattern analysis (MVPA) has gained much popularity in the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data. However, MVPA may appear daunting to those who have been applying traditional analyses using event-related potentials (ERPs) or event-related fields (ERFs). To ease this transition, we recently developed the Amsterdam Decoding and Modeling (ADAM) toolbox in MATLAB. ADAM is an entry-level toolbox that allows a direct comparison of ERP/ERF results to MVPA results using any dataset in standard EEGLAB or Fieldtrip format. The toolbox performs and visualizes multiple-comparison corrected group decoding and forward encoding results in a variety of ways, such as classifier performance across time, temporal generalization (time-by-time) matrices of classifier performance, channel tuning functions (CTFs) and topographical maps of (forward-transformed) classifier weights. All analyses can be performed directly on raw data or can be preceded by a time-frequency decomposition of the data in which case the analyses are performed separately on different frequency bands. The figures ADAM produces are publication-ready. In the current manuscript, we provide a cookbook in which we apply a decoding analysis to a publicly available MEG/EEG dataset involving the perception of famous, non-famous and scrambled faces. The manuscript covers the steps involved in single subject analysis and shows how to perform and visualize a subsequent group-level statistical analysis. The processing pipeline covers computation and visualization of group ERPs, ERP difference waves, as well as MVPA decoding results. It ends with a comparison of the differences and similarities between EEG and MEG decoding results. The manuscript has a level of description that allows application of these analyses to any dataset in EEGLAB or Fieldtrip format. |
X Demographics
The data shown below were collected from the profiles of 66 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 10 | 15% |
| United Kingdom | 8 | 12% |
| United States | 6 | 9% |
| Germany | 5 | 8% |
| India | 2 | 3% |
| Switzerland | 2 | 3% |
| Australia | 2 | 3% |
| Canada | 2 | 3% |
| Denmark | 1 | 2% |
| Other | 7 | 11% |
| Unknown | 21 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 37 | 56% |
| Scientists | 27 | 41% |
| Practitioners (doctors, other healthcare professionals) | 2 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 254 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 254 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 48 | 19% |
| Student > Master | 46 | 18% |
| Student > Ph. D. Student | 39 | 15% |
| Student > Doctoral Student | 13 | 5% |
| Student > Bachelor | 13 | 5% |
| Other | 29 | 11% |
| Unknown | 66 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 74 | 29% |
| Neuroscience | 54 | 21% |
| Agricultural and Biological Sciences | 9 | 4% |
| Computer Science | 8 | 3% |
| Linguistics | 7 | 3% |
| Other | 20 | 8% |
| Unknown | 82 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 40. 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 02 February 2021.
All research outputs
#1,022,785
of 25,394,764 outputs
Outputs from Frontiers in Neuroscience
#437
of 11,544 outputs
Outputs of similar age
#21,868
of 341,383 outputs
Outputs of similar age from Frontiers in Neuroscience
#15
of 235 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,544 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done particularly well, scoring higher than 96% 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 341,383 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 235 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.