Evidence map›Paper›PMID 33375441›Full record

ArticleSensors (Basel, Switzerland)2020

Evaluating the Effect of Stimuli Color and Frequency on SSVEP.

Xavier Duart, Eduardo Quiles, Ferran Suay, Nayibe Chio, Emilio García, Francisco Morant

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 2 countries.

Xavier DuartDepartament de Psicobiologia, Facultat de Psicologia, Universitat de València, 46010 València, Spain.
Eduardo QuilesInstituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.ORCID 0000-0003-0578-4716
Ferran SuayDepartament de Psicobiologia, Facultat de Psicologia, Universitat de València, 46010 València, Spain.
Nayibe ChioInstituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.ORCID 0000-0002-9459-4350
Emilio GarcíaInstituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.
Francisco MorantInstituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.
Universitat Politècnica de València · ESUniversitat de València · ESUniversidad Autónoma de Bucaramanga · CO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain-computer interfaces (BCI) can extract information about the subject's intentions by registering and processing electroencephalographic (EEG) signals to generate actions on physical systems. Steady-state visual-evoked potentials (SSVEP) are produced when the subject stares at flashing visual stimuli. By means of spectral analysis and by measuring the signal-to-noise ratio (SNR) of its harmonic contents, the observed stimulus can be identified. Stimulus color matters, and some authors have proposed red because of its ability to capture attention, while others refuse it because it might induce epileptic seizures. Green has also been proposed and it is claimed that white may generate the best signals. Regarding frequency, middle frequencies are claimed to produce the best SNR, although high frequencies have not been thoroughly studied, and might be advantageous due to the lower spontaneous cerebral activity in this frequency band. Here, we show white, red, and green stimuli, at three frequencies: 5 (low), 12 (middle), and 30 (high) Hz to 42 subjects, and compare them in order to find which one can produce the best SNR. We aim to know if the response to white is as strong as the one to red, and also if the response to high frequency is as strong as the one triggered by lower frequencies. Attention has been measured with the Conner's Continuous Performance Task version 2 (CPT-II) task, in order to search for a potential relationship between attentional capacity and the SNR previously obtained. An analysis of variance (ANOVA) shows the best SNR with the middle frequency, followed by the low, and finally the high one. White gives as good an SNR as red at 12 Hz and so does green at 5 Hz, with no differences at 30 Hz. These results suggest that middle frequencies are preferable and that using the red color can be avoided. Correlation analysis also show a correlation between attention and the SNR at low frequency, so suggesting that for the low frequencies, more attentional capacity leads to better results.

Indexed as

biomedical signal processingbrain–computer interfaceelectroencephalographySSVEP

Identifiers

PMID33375441
PMCPMC7796402
OpenAlexW3117640341

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.