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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A New Method to Generate Artificial Frames Using the Empirical Mode Decomposition for an EEG-Based Motor Imagery BCI
|
|---|---|
| Published in |
Frontiers in Neuroscience, May 2018
|
| DOI | 10.3389/fnins.2018.00308 |
| Pubmed ID | |
| Authors |
Josep Dinarès-Ferran, Rupert Ortner, Christoph Guger, Jordi Solé-Casals |
| Abstract |
EEG-based Brain-Computer Interfaces (BCIs) are becoming a new tool for neurorehabilitation. BCIs are used to help stroke patients to improve the functional capability of the impaired limbs, and to communicate and assess the level of consciousness in Disorder of Consciousness (DoC) patients. BCIs based on a motor imagery paradigm typically require a training period to adapt the system to each user's brain, and the BCI then creates and uses a classifier created with the acquired EEG. The quality of this classifier relies on amount of data used for training. More data can improve the classifier, but also increases the training time, which can be especially problematic for some patients. Training time might be reduced by creating new artificial frames by applying Empirical Mode Decomposition (EMD) on the EEG frames and mixing their Intrinsic Mode Function (IMFs). The purpose of this study is to explore the use of artificial EEG frames as replacements for some real ones by comparing classifiers trained with some artificial frames to classifiers trained with only real data. Results showed that, in some subjects, it is possible to replace up to 50% of frames with artificial data, which reduces training time from 720 to 360 s. In the remaining subjects, at least 12.5% of the real EEG frames could be replaced, reducing the training time by 90 s. Moreover, the method can be used to replace EEG frames that contain artifact, which reduces the impact of rejecting data with artifact. The method was also tested on an out of sample scenario with the best subjects from a public database, who yielded very good results using a frame collection with 87.5% artificial frames. These initial results with healthy users need to be further explored with patients' data, along with research into alternative IMF mixing strategies and using other BCI paradigms. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 3 | 23% |
| France | 2 | 15% |
| Colombia | 1 | 8% |
| Germany | 1 | 8% |
| Switzerland | 1 | 8% |
| Unknown | 5 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 64 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 17% |
| Student > Master | 8 | 13% |
| Researcher | 5 | 8% |
| Other | 5 | 8% |
| Professor | 4 | 6% |
| Other | 10 | 16% |
| Unknown | 21 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 15 | 23% |
| Computer Science | 6 | 9% |
| Neuroscience | 6 | 9% |
| Medicine and Dentistry | 4 | 6% |
| Sports and Recreations | 2 | 3% |
| Other | 4 | 6% |
| Unknown | 27 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 29 May 2018.
All research outputs
#4,575,167
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#3,547
of 11,542 outputs
Outputs of similar age
#81,834
of 339,382 outputs
Outputs of similar age from Frontiers in Neuroscience
#78
of 237 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,542 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 gotten more attention than average, scoring higher than 69% 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 339,382 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 237 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 67% of its contemporaries.