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Development of an electrooculogram-based eye-computer interface for communication of individuals with amyotrophic lateral sclerosis

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, September 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)

Mentioned by

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4 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

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16 Dimensions

Readers on

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60 Mendeley
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Title
Development of an electrooculogram-based eye-computer interface for communication of individuals with amyotrophic lateral sclerosis
Published in
Journal of NeuroEngineering and Rehabilitation, September 2017
DOI 10.1186/s12984-017-0303-5
Pubmed ID
Authors

Won-Du Chang, Ho-Seung Cha, Do Yeon Kim, Seung Hyun Kim, Chang-Hwan Im

Abstract

Electrooculogram (EOG) can be used to continuously track eye movements and can thus be considered as an alternative to conventional camera-based eye trackers. Although many EOG-based eye tracking systems have been studied with the ultimate goal of providing a new way of communication for individuals with amyotrophic lateral sclerosis (ALS), most of them were tested with healthy people only. In this paper, we investigated the feasibility of EOG-based eye-writing as a new mode of communication for individuals with ALS. We developed an EOG-based eye-writing system and tested this system with 18 healthy participants and three participants with ALS. We also applied a new method for removing crosstalk between horizontal and vertical EOG components. All study participants were asked to eye-write specially designed patterns of 10 Arabic numbers three times after a short practice session. Our system achieved a mean recognition rates of 95.93% for healthy participants and showed recognition rates of 95.00%, 66.67%, and 93.33% for the three participants with ALS. The low recognition rates in one of the participants with ALS was mainly due to miswritten letters, the number of which decreased as the experiment proceeded. Our proposed eye-writing system is a feasible human-computer interface (HCI) tool for enabling practical communication of individuals with ALS.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 12 20%
Researcher 5 8%
Student > Ph. D. Student 5 8%
Student > Master 5 8%
Student > Doctoral Student 3 5%
Other 6 10%
Unknown 24 40%
Readers by discipline Count As %
Engineering 14 23%
Medicine and Dentistry 6 10%
Nursing and Health Professions 6 10%
Neuroscience 3 5%
Unspecified 2 3%
Other 5 8%
Unknown 24 40%

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 10 April 2018.
All research outputs
#4,984,531
of 15,922,255 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#346
of 965 outputs
Outputs of similar age
#99,831
of 279,566 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#1
of 4 outputs
Altmetric has tracked 15,922,255 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 965 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has gotten more attention than average, scoring higher than 63% 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 279,566 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 63% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them