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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset
|
|---|---|
| Published in |
Journal of NeuroEngineering and Rehabilitation, August 2014
|
| DOI | 10.1186/1743-0003-11-119 |
| Pubmed ID | |
| Authors |
Yuan-Pin Lin, Yijun Wang, Tzyy-Ping Jung |
| Abstract |
Bridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals' EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 2% |
| Netherlands | 1 | 1% |
| France | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 85 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 26% |
| Researcher | 17 | 19% |
| Student > Master | 15 | 17% |
| Student > Bachelor | 9 | 10% |
| Professor > Associate Professor | 4 | 4% |
| Other | 11 | 12% |
| Unknown | 11 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 21 | 23% |
| Computer Science | 14 | 16% |
| Neuroscience | 9 | 10% |
| Psychology | 9 | 10% |
| Medicine and Dentistry | 6 | 7% |
| Other | 10 | 11% |
| Unknown | 21 | 23% |
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 14 August 2014.
All research outputs
#18,376,056
of 22,760,687 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#983
of 1,278 outputs
Outputs of similar age
#164,440
of 230,541 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#15
of 18 outputs
Altmetric has tracked 22,760,687 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,278 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one is in the 11th percentile – i.e., 11% 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 230,541 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.