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Detection of self-paced reaching movement intention from EEG signals

Overview of attention for article published in Frontiers in Neuroengineering, January 2012
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  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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Title
Detection of self-paced reaching movement intention from EEG signals
Published in
Frontiers in Neuroengineering, January 2012
DOI 10.3389/fneng.2012.00013
Pubmed ID
Authors

Eileen Lew, Ricardo Chavarriaga, Stefano Silvoni, José del R. Millán

Abstract

Future neuroprosthetic devices, in particular upper limb, will require decoding and executing not only the user's intended movement type, but also when the user intends to execute the movement. This work investigates the potential use of brain signals recorded non-invasively for detecting the time before a self-paced reaching movement is initiated which could contribute to the design of practical upper limb neuroprosthetics. In particular, we show the detection of self-paced reaching movement intention in single trials using the readiness potential, an electroencephalography (EEG) slow cortical potential (SCP) computed in a narrow frequency range (0.1-1 Hz). Our experiments with 12 human volunteers, two of them stroke subjects, yield high detection rates prior to the movement onset and low detection rates during the non-movement intention period. With the proposed approach, movement intention was detected around 500 ms before actual onset, which clearly matches previous literature on readiness potentials. Interestingly, the result obtained with one of the stroke subjects is coherent with those achieved in healthy subjects, with single-trial performance of up to 92% for the paretic arm. These results suggest that, apart from contributing to our understanding of voluntary motor control for designing more advanced neuroprostheses, our work could also have a direct impact on advancing robot-assisted neurorehabilitation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 293 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
Germany 3 1%
Brazil 2 <1%
Netherlands 1 <1%
France 1 <1%
Switzerland 1 <1%
Hungary 1 <1%
Italy 1 <1%
India 1 <1%
Other 1 <1%
Unknown 276 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 75 26%
Student > Master 50 17%
Researcher 40 14%
Student > Bachelor 23 8%
Student > Doctoral Student 19 6%
Other 40 14%
Unknown 46 16%
Readers by discipline Count As %
Engineering 103 35%
Neuroscience 32 11%
Agricultural and Biological Sciences 23 8%
Computer Science 20 7%
Psychology 17 6%
Other 33 11%
Unknown 65 22%
Attention Score in Context

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 22 February 2018.
All research outputs
#6,007,020
of 22,675,759 outputs
Outputs from Frontiers in Neuroengineering
#24
of 82 outputs
Outputs of similar age
#54,299
of 244,088 outputs
Outputs of similar age from Frontiers in Neuroengineering
#3
of 17 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 82 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has gotten more attention than average, scoring higher than 70% 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 244,088 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 77% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.