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Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG)

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, December 2014
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  • Average Attention Score compared to outputs of the same age and source

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1 Facebook page

Citations

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64 Dimensions

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149 Mendeley
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Title
Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG)
Published in
Journal of NeuroEngineering and Rehabilitation, December 2014
DOI 10.1186/1743-0003-11-165
Pubmed ID
Authors

Matthias Witkowski, Mario Cortese, Marco Cempini, Jürgen Mellinger, Nicola Vitiello, Surjo R Soekadar

Abstract

Brain-machine interfaces (BMIs) allow direct translation of electric, magnetic or metabolic brain signals into control commands of external devices such as robots, prostheses or exoskeletons. However, non-stationarity of brain signals and susceptibility to biological or environmental artifacts impede reliable control and safety of BMIs, particularly in daily life environments. Here we introduce and tested a novel hybrid brain-neural computer interaction (BNCI) system fusing electroencephalography (EEG) and electrooculography (EOG) to enhance reliability and safety of continuous hand exoskeleton-driven grasping motions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Hungary 1 <1%
Germany 1 <1%
Egypt 1 <1%
Unknown 143 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 29 19%
Researcher 21 14%
Student > Ph. D. Student 21 14%
Student > Bachelor 10 7%
Student > Doctoral Student 8 5%
Other 18 12%
Unknown 42 28%
Readers by discipline Count As %
Engineering 50 34%
Neuroscience 12 8%
Computer Science 11 7%
Agricultural and Biological Sciences 5 3%
Psychology 4 3%
Other 15 10%
Unknown 52 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 04 June 2015.
All research outputs
#7,392,570
of 22,774,233 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#481
of 1,278 outputs
Outputs of similar age
#104,758
of 354,373 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#17
of 33 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
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 has gotten more attention than average, scoring higher than 61% 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 354,373 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.