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Analyzing EEG signals to detect unexpected obstacles during walking

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, January 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

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5 tweeters

Citations

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20 Dimensions

Readers on

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96 Mendeley
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1 CiteULike
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Title
Analyzing EEG signals to detect unexpected obstacles during walking
Published in
Journal of NeuroEngineering and Rehabilitation, January 2015
DOI 10.1186/s12984-015-0095-4
Pubmed ID
Authors

R. Salazar-Varas, R. Salazar-Varas, Á. Costa, E. Iáñez, A. Úbeda, E. Hortal, J. M. Azorín

Abstract

When an unexpected perturbation in the environment occurs, the subsequent alertness state may cause a brain activation responding to that perturbation which can be detected and employed by a Brain-Computer Interface (BCI). In this work, the possibility of detecting a sudden obstacle appearance analyzing electroencephalographic (EEG) signals is assessed. For this purpose, different features of EEG signals are evaluated during the appearance of sudden obstacles while a subject is walking on a treadmill. The future goal is to use this procedure to detect any obstacle appearance during walking when the user is wearing a lower limb exoskeleton in order to generate an emergency stop command for the exoskeleton. This would enhance the user-exoskeleton interaction, improving the safety mechanisms of current exoskeletons. In order to detect the change in the brain activity when an obstacle suddenly appears, different features of EEG signals are evaluated using the recordings of five healthy subjects. Since the change in the brain activity occurs in the time domain, the features evaluated are: common spatial patterns, average power, slope, and the coefficients of a polynomial fit. A Linear Discriminant Analysis-based classifier is used to differentiate between two conditions: the appearance or not of an obstacle. The evaluation of the performance to detect the obstacles is made in terms of accuracy, true positive (TP) and false positive (FP) rates. From the offline analysis, the best performance is achieved when the slope or the polynomial coefficients are used as features, with average detection accuracy rates of 74.0 and 79.5 %, respectively. These results are consistent with the pseudo-online results, where a complete EEG recording is segmented into windows of 500 ms and overlapped 400 ms, and a decision about the obstacle appearance is made for each window. The results of the best subject were 11 out of 14 obstacles detected with a rate of 9.09 FPs/min, and 10 out of 14 obstacles detected with a rate of 6.34 FPs/min using slope and polynomial coefficients features, respectively. An EEG-based BCI can be developed to detect the appearance of unexpected obstacles. The average accuracy achieved is 79.5 % of success rate with a low number of false detections. Thus, the online performance of the BCI would be suitable for commanding in a safely way a lower limb exoskeleton during walking.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Germany 1 1%
Unknown 94 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 21%
Researcher 15 16%
Student > Master 15 16%
Student > Doctoral Student 10 10%
Student > Bachelor 5 5%
Other 16 17%
Unknown 15 16%
Readers by discipline Count As %
Engineering 33 34%
Neuroscience 14 15%
Medicine and Dentistry 12 13%
Psychology 4 4%
Sports and Recreations 4 4%
Other 7 7%
Unknown 22 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 24 November 2015.
All research outputs
#5,483,643
of 19,863,675 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#341
of 1,197 outputs
Outputs of similar age
#102,817
of 391,281 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#20
of 67 outputs
Altmetric has tracked 19,863,675 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,197 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has gotten more attention than average, scoring higher than 71% 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 391,281 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 73% of its contemporaries.
We're also able to compare this research output to 67 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 71% of its contemporaries.