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

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...

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Autores principales: Kartsch, Victor Javier, Kumaravel, Velu Prabhakar, Benatti, Simone, Vallortigara, Giorgio, Benini, Luca, Farella, Elisabetta, Buiatti, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785135/
https://www.ncbi.nlm.nih.gov/pubmed/36560172
http://dx.doi.org/10.3390/s22249803
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author Kartsch, Victor Javier
Kumaravel, Velu Prabhakar
Benatti, Simone
Vallortigara, Giorgio
Benini, Luca
Farella, Elisabetta
Buiatti, Marco
author_facet Kartsch, Victor Javier
Kumaravel, Velu Prabhakar
Benatti, Simone
Vallortigara, Giorgio
Benini, Luca
Farella, Elisabetta
Buiatti, Marco
author_sort Kartsch, Victor Javier
collection PubMed
description 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.
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spelling pubmed-97851352022-12-24 Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis Kartsch, Victor Javier Kumaravel, Velu Prabhakar Benatti, Simone Vallortigara, Giorgio Benini, Luca Farella, Elisabetta Buiatti, Marco Sensors (Basel) Article 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. MDPI 2022-12-14 /pmc/articles/PMC9785135/ /pubmed/36560172 http://dx.doi.org/10.3390/s22249803 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kartsch, Victor Javier
Kumaravel, Velu Prabhakar
Benatti, Simone
Vallortigara, Giorgio
Benini, Luca
Farella, Elisabetta
Buiatti, Marco
Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis
title Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis
title_full Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis
title_fullStr Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis
title_full_unstemmed Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis
title_short Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis
title_sort efficient low-frequency ssvep detection with wearable eeg using normalized canonical correlation analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785135/
https://www.ncbi.nlm.nih.gov/pubmed/36560172
http://dx.doi.org/10.3390/s22249803
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