↓ Skip to main content

What limits the performance of current invasive brain machine interfaces?

Overview of attention for article published in Frontiers in Systems Neuroscience, April 2014
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

news
5 news outlets
twitter
11 X users
googleplus
1 Google+ user

Readers on

mendeley
147 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
What limits the performance of current invasive brain machine interfaces?
Published in
Frontiers in Systems Neuroscience, April 2014
DOI 10.3389/fnsys.2014.00068
Pubmed ID
Authors

Gytis Baranauskas

Abstract

The concept of a brain-machine interface (BMI) or a computer-brain interface is simple: BMI creates a communication pathway for a direct control by brain of an external device. In reality BMIs are very complex devices and only recently the increase in computing power of microprocessors enabled a boom in BMI research that continues almost unabated to this date, the high point being the insertion of electrode arrays into the brains of 5 human patients in a clinical trial run by Cyberkinetics with few other clinical tests still in progress. Meanwhile several EEG-based BMI devices (non-invasive BMIs) were launched commercially. Modern electronics and dry electrode technology made possible to drive the cost of some of these devices below few hundred dollars. However, the initial excitement of the direct control by brain waves of a computer or other equipment is dampened by large efforts required for learning, high error rates and slow response speed. All these problems are directly related to low information transfer rates typical for such EEG-based BMIs. In invasive BMIs employing multiple electrodes inserted into the brain one may expect much higher information transfer rates than in EEG-based BMIs because, in theory, each electrode provides an independent information channel. However, although invasive BMIs require more expensive equipment and have ethical problems related to the need to insert electrodes in the live brain, such financial and ethical costs are often not offset by a dramatic improvement in the information transfer rate. Thus the main topic of this review is why in invasive BMIs an apparently much larger information content obtained with multiple extracellular electrodes does not translate into much higher rates of information transfer? This paper explores possible answers to this question by concluding that more research on what movement parameters are encoded by neurons in motor cortex is needed before we can enjoy the next generation BMIs.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
France 2 1%
Finland 1 <1%
Singapore 1 <1%
United Kingdom 1 <1%
Unknown 137 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 20%
Researcher 26 18%
Student > Master 21 14%
Student > Bachelor 17 12%
Other 8 5%
Other 26 18%
Unknown 19 13%
Readers by discipline Count As %
Engineering 39 27%
Neuroscience 24 16%
Agricultural and Biological Sciences 20 14%
Medicine and Dentistry 13 9%
Computer Science 13 9%
Other 15 10%
Unknown 23 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 47. 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 03 April 2017.
All research outputs
#867,294
of 25,182,110 outputs
Outputs from Frontiers in Systems Neuroscience
#57
of 1,404 outputs
Outputs of similar age
#8,173
of 233,865 outputs
Outputs of similar age from Frontiers in Systems Neuroscience
#7
of 59 outputs
Altmetric has tracked 25,182,110 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,404 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has done particularly well, scoring higher than 95% 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 233,865 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.