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Single-trial event-related potential extraction through one-unit ICA-with-reference

Overview of attention for article published in Journal of Neural Engineering, October 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

twitter
6 tweeters

Citations

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3 Dimensions

Readers on

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16 Mendeley
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Title
Single-trial event-related potential extraction through one-unit ICA-with-reference
Published in
Journal of Neural Engineering, October 2016
DOI 10.1088/1741-2560/13/6/066010
Pubmed ID
Authors

Wee Lih Lee, Tele Tan, Torbjörn Falkmer, Yee Hong Leung

Abstract

In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed. In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly. Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction. In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Algeria 1 6%
Korea, Republic of 1 6%
Unknown 14 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 38%
Student > Master 3 19%
Professor 2 13%
Unspecified 2 13%
Researcher 1 6%
Other 2 13%
Readers by discipline Count As %
Engineering 11 69%
Unspecified 2 13%
Neuroscience 2 13%
Computer Science 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 15 October 2016.
All research outputs
#2,198,980
of 8,518,079 outputs
Outputs from Journal of Neural Engineering
#228
of 589 outputs
Outputs of similar age
#86,234
of 252,673 outputs
Outputs of similar age from Journal of Neural Engineering
#5
of 19 outputs
Altmetric has tracked 8,518,079 research outputs across all sources so far. This one has received more attention than most of these and is in the 60th percentile.
So far Altmetric has tracked 589 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.0. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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 252,673 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 64% of its contemporaries.
We're also able to compare this research output to 19 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 52% of its contemporaries.