↓ Skip to main content


Article Metrics

Intention Concepts and Brain-Machine Interfacing

Overview of attention for article published in Frontiers in Psychology, January 2012
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

13 tweeters
1 Google+ user


14 Dimensions

Readers on

68 Mendeley
Intention Concepts and Brain-Machine Interfacing
Published in
Frontiers in Psychology, January 2012
DOI 10.3389/fpsyg.2012.00455
Pubmed ID

Franziska Thinnes-Elker, Olga Iljina, John Kyle Apostolides, Felicitas Kraemer, Andreas Schulze-Bonhage, Ad Aertsen, Tonio Ball


Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs) that are currently being developed to restore speech and motor control in paralyzed patients. Such BMI devices record the brain activity of the agent, interpret ("decode") the agent's intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent's intentions from neural signals in practical BMI applications.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 2 3%
Spain 1 1%
China 1 1%
France 1 1%
Unknown 63 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 22%
Researcher 14 21%
Student > Doctoral Student 6 9%
Student > Master 6 9%
Professor 5 7%
Other 13 19%
Unknown 9 13%
Readers by discipline Count As %
Psychology 15 22%
Engineering 11 16%
Agricultural and Biological Sciences 7 10%
Neuroscience 6 9%
Medicine and Dentistry 5 7%
Other 12 18%
Unknown 12 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 25 April 2013.
All research outputs
of 20,773,675 outputs
Outputs from Frontiers in Psychology
of 24,317 outputs
Outputs of similar age
of 174,004 outputs
Outputs of similar age from Frontiers in Psychology
of 1 outputs
Altmetric has tracked 20,773,675 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 24,317 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.9. This one has done well, scoring higher than 79% 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 174,004 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