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Nonlinear Origin of SSVEP Spectra—A Combined Experimental and Modeling Study

Overview of attention for article published in Frontiers in Computational Neuroscience, December 2016
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Title
Nonlinear Origin of SSVEP Spectra—A Combined Experimental and Modeling Study
Published in
Frontiers in Computational Neuroscience, December 2016
DOI 10.3389/fncom.2016.00129
Pubmed ID
Authors

Maciej Labecki, Rafal Kus, Alicja Brzozowska, Tadeusz Stacewicz, Basabdatta S. Bhattacharya, Piotr Suffczynski

Abstract

Steady state visual evoked potentials (SSVEPs) are steady state oscillatory potentials elicited in the electroencephalogram (EEG) by flicker stimulation. The frequency of these responses maches the frequency of the stimulation and of its harmonics and subharmonics. In this study, we investigated the origin of the harmonic and subharmonic components of SSVEPs, which are not well understood. We applied both sine and square wave visual stimulation at 5 and 15 Hz to human subjects and analyzed the properties of the fundamental responses and harmonically related components. In order to interpret the results, we used the well-established neural mass model that consists of interacting populations of excitatory and inhibitory cortical neurons. In our study, this model provided a simple explanation for the origin of SSVEP spectra, and showed that their harmonic and subharmonic components are a natural consequence of the nonlinear properties of neuronal populations and the resonant properties of the modeled network. The model also predicted multiples of subharmonic responses, which were subsequently confirmed using experimental data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
United States 1 1%
Unknown 67 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 33%
Researcher 8 12%
Student > Master 7 10%
Professor 6 9%
Student > Bachelor 5 7%
Other 8 12%
Unknown 12 17%
Readers by discipline Count As %
Engineering 16 23%
Neuroscience 14 20%
Psychology 9 13%
Agricultural and Biological Sciences 4 6%
Computer Science 2 3%
Other 5 7%
Unknown 19 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 January 2017.
All research outputs
#20,376,559
of 22,925,760 outputs
Outputs from Frontiers in Computational Neuroscience
#1,161
of 1,347 outputs
Outputs of similar age
#355,573
of 420,925 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#31
of 36 outputs
Altmetric has tracked 22,925,760 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,347 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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