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Reducing Brain Signal Noise in the Prediction of Economic Choices: A Case Study in Neuroeconomics

Overview of attention for article published in Frontiers in Neuroscience, December 2017
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  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

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Title
Reducing Brain Signal Noise in the Prediction of Economic Choices: A Case Study in Neuroeconomics
Published in
Frontiers in Neuroscience, December 2017
DOI 10.3389/fnins.2017.00704
Pubmed ID
Authors

Raanju R. Sundararajan, Marco A. Palma, Mohsen Pourahmadi

Abstract

In order to reduce the noise of brain signals, neuroeconomic experiments typically aggregate data from hundreds of trials collected from a few individuals. This contrasts with the principle of simple and controlled designs in experimental and behavioral economics. We use a frequency domain variant of the stationary subspace analysis (SSA) technique, denoted as DSSA, to filter out the noise (nonstationary sources) in EEG brain signals. The nonstationary sources in the brain signal are associated with variations in the mental state that are unrelated to the experimental task. DSSA is a powerful tool for reducing the number of trials needed from each participant in neuroeconomic experiments and also for improving the prediction performance of an economic choice task. For a single trial, when DSSA is used as a noise reduction technique, the prediction model in a food snack choice experiment has an increase in overall accuracy by around 10% and in sensitivity and specificity by around 20% and in AUC by around 30%, respectively.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 14%
Student > Ph. D. Student 4 11%
Professor 2 6%
Student > Doctoral Student 2 6%
Other 1 3%
Other 5 14%
Unknown 16 46%
Readers by discipline Count As %
Business, Management and Accounting 4 11%
Economics, Econometrics and Finance 3 9%
Engineering 3 9%
Unspecified 1 3%
Decision Sciences 1 3%
Other 3 9%
Unknown 20 57%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 December 2017.
All research outputs
#8,190,103
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#5,174
of 11,542 outputs
Outputs of similar age
#149,384
of 443,583 outputs
Outputs of similar age from Frontiers in Neuroscience
#75
of 187 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has gotten more attention than average, scoring higher than 54% 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 443,583 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 65% of its contemporaries.
We're also able to compare this research output to 187 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 59% of its contemporaries.