Evidence map›Paper›PMID 36560172›Full record

ArticleSensors (Basel, Switzerland)2022

Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis.

Victor Javier Kartsch, Velu Prabhakar Kumaravel, Simone Benatti, Giorgio Vallortigara, Luca Benini, Elisabetta Farella, Marco Buiatti

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
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

7 authors.

Victor Javier KartschDepartment of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy.ORCID 0000-0003-3325-6347
Velu Prabhakar KumaravelDigital Society Center, Fondazione Bruno Kessler, 38123 Trento, Italy.ORCID 0000-0002-9286-4687
Simone BenattiDepartment of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy.ORCID 0000-0003-2807-3618
Giorgio VallortigaraCenter for Mind/Brain Sciences (CIMeC), University of Trento, 38068 Rovereto, Italy.ORCID 0000-0001-8192-9062
Luca BeniniDepartment of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy.ORCID 0000-0001-8068-3806
Elisabetta FarellaDigital Society Center, Fondazione Bruno Kessler, 38123 Trento, Italy.ORCID 0000-0001-9047-9868
Marco BuiattiCenter for Mind/Brain Sciences (CIMeC), University of Trento, 38068 Rovereto, Italy.ORCID 0000-0003-2362-7853

Funding

European Research Council 842243
6 · The paper itself

Abstract

Recent studies show that the integrity of core perceptual and cognitive functions may be tested in a short time with Steady-State Visual Evoked Potentials (SSVEP) with low stimulation frequencies, between 1 and 10 Hz. Wearable EEG systems provide unique opportunities to test these brain functions on diverse populations in out-of-the-lab conditions. However, they also pose significant challenges as the number of EEG channels is typically limited, and the recording conditions might induce high noise levels, particularly for low frequencies. Here we tested the performance of Normalized Canonical Correlation Analysis (NCCA), a frequency-normalized version of CCA, to quantify SSVEP from wearable EEG data with stimulation frequencies ranging from 1 to 10 Hz. We validated NCCA on data collected with an 8-channel wearable wireless EEG system based on BioWolf, a compact, ultra-light, ultra-low-power recording platform. The results show that NCCA correctly and rapidly detects SSVEP at the stimulation frequency within a few cycles of stimulation, even at the lowest frequency (4 s recordings are sufficient for a stimulation frequency of 1 Hz), outperforming a state-of-the-art normalized power spectral measure. Importantly, no preliminary artifact correction or channel selection was required. Potential applications of these results to research and clinical studies are discussed.

Indexed as

Brain-Computer InterfacesWearable Electronic DevicesAlgorithmsCanonical Correlation AnalysisElectroencephalographyEvoked Potentials, VisualPhotic StimulationCCAdelta bandfrequency taggingNCCASSVEPtheta bandwearable EEG

Identifiers

PMID36560172
PMCPMC9785135

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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.