↓ Skip to main content

Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system

Overview of attention for article published in BMC Neurology, December 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

news
1 news outlet
twitter
4 tweeters

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
70 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system
Published in
BMC Neurology, December 2015
DOI 10.1186/s12883-015-0521-z
Pubmed ID
Authors

Yuanqing Li, Jiahui Pan, Yanbin He, Fei Wang, Steven Laureys, Qiuyou Xie, Ronghao Yu

Abstract

For patients with disorders of consciousness such as coma, a vegetative state or a minimally conscious state, one challenge is to detect and assess the residual cognitive functions in their brains. Number processing and mental calculation are important brain functions but are difficult to detect in patients with disorders of consciousness using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised due to the patients' motor impairments and inability to provide sufficient motor responses for number- and calculation-based communication. In this study, we presented a hybrid brain-computer interface that combines P300 and steady state visual evoked potentials to detect number processing and mental calculation in Han Chinese patients with disorders of consciousness. Eleven patients with disorders of consciousness who were in a vegetative state (n = 6) or in a minimally conscious state (n = 3) or who emerged from a minimally conscious state (n = 2) participated in the brain-computer interface-based experiment. During the experiment, the patients with disorders of consciousness were instructed to perform three tasks, i.e., number recognition, number comparison, and mental calculation, including addition and subtraction. In each experimental trial, an arithmetic problem was first presented. Next, two number buttons, only one of which was the correct answer to the problem, flickered at different frequencies to evoke steady state visual evoked potentials, while the frames of the two buttons flashed in a random order to evoke P300 potentials. The patients needed to focus on the target number button (the correct answer). Finally, the brain-computer interface system detected P300 and steady state visual evoked potentials to determine the button to which the patients attended, further presenting the results as feedback. Two of the six patients who were in a vegetative state, one of the three patients who were in a minimally conscious state, and the two patients that emerged from a minimally conscious state achieved accuracies significantly greater than the chance level. Furthermore, P300 potentials and steady state visual evoked potentials were observed in the electroencephalography signals from the five patients. Number processing and arithmetic abilities as well as command following were demonstrated in the five patients. Furthermore, our results suggested that through brain-computer interface systems, many cognitive experiments may be conducted in patients with disorders of consciousness, although they cannot provide sufficient behavioral responses.

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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 14%
Student > Bachelor 10 14%
Student > Master 9 13%
Researcher 8 11%
Student > Postgraduate 5 7%
Other 10 14%
Unknown 18 26%
Readers by discipline Count As %
Neuroscience 13 19%
Psychology 11 16%
Agricultural and Biological Sciences 7 10%
Medicine and Dentistry 6 9%
Engineering 4 6%
Other 8 11%
Unknown 21 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 April 2020.
All research outputs
#2,093,801
of 19,035,438 outputs
Outputs from BMC Neurology
#198
of 2,056 outputs
Outputs of similar age
#42,717
of 356,916 outputs
Outputs of similar age from BMC Neurology
#1
of 1 outputs
Altmetric has tracked 19,035,438 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,056 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one has done particularly well, scoring higher than 90% 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 356,916 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 87% of its contemporaries.
We're also able to compare this research output to 1 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