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A multi-day and multi-band dataset for a steady-state visual-evoked potential–based brain-computer interface

BACKGROUND: A steady-state visual-evoked potential (SSVEP) is a brain response to visual stimuli modulated at certain frequencies; it has been widely used in electroencephalography (EEG)-based brain–computer interface research. However, there are few published SSVEP datasets for brain–computer inter...

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Autores principales: Choi, Ga-Young, Han, Chang-Hee, Jung, Young-Jin, Hwang, Han-Jeong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6876666/
https://www.ncbi.nlm.nih.gov/pubmed/31765472
http://dx.doi.org/10.1093/gigascience/giz133
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author Choi, Ga-Young
Han, Chang-Hee
Jung, Young-Jin
Hwang, Han-Jeong
author_facet Choi, Ga-Young
Han, Chang-Hee
Jung, Young-Jin
Hwang, Han-Jeong
author_sort Choi, Ga-Young
collection PubMed
description BACKGROUND: A steady-state visual-evoked potential (SSVEP) is a brain response to visual stimuli modulated at certain frequencies; it has been widely used in electroencephalography (EEG)-based brain–computer interface research. However, there are few published SSVEP datasets for brain–computer interface. In this study, we obtained a new SSVEP dataset based on measurements from 30 participants, performed on 2 days; our dataset complements existing SSVEP datasets: (i) multi-band SSVEP datasets are provided, and all 3 possible frequency bands (low, middle, and high) were used for SSVEP stimulation; (ii) multi-day datasets are included; and (iii) the EEG datasets include simultaneously obtained physiological measurements, such as respiration, electrocardiography, electromyography, and head motion (accelerator). FINDINGS: To validate our dataset, we estimated the spectral powers and classification performance for the EEG (SSVEP) datasets and created an example plot to visualize the physiological time-series data. Strong SSVEP responses were observed at stimulation frequencies, and the mean classification performance of the middle frequency band was significantly higher than the low- and high-frequency bands. Other physiological data also showed reasonable results. CONCLUSIONS: Our multi-band, multi-day SSVEP datasets can be used to optimize stimulation frequencies because they enable simultaneous investigation of the characteristics of the SSVEPs evoked in each of the 3 frequency bands, and solve session-to-session (day-to-day) transfer problems by enabling investigation of the non-stationarity of SSVEPs measured on different days. Additionally, auxiliary physiological data can be used to explore the relationship between SSVEP characteristics and physiological conditions, providing useful information for optimizing experimental paradigms to achieve high performance.
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spelling pubmed-68766662019-11-27 A multi-day and multi-band dataset for a steady-state visual-evoked potential–based brain-computer interface Choi, Ga-Young Han, Chang-Hee Jung, Young-Jin Hwang, Han-Jeong Gigascience Data Note BACKGROUND: A steady-state visual-evoked potential (SSVEP) is a brain response to visual stimuli modulated at certain frequencies; it has been widely used in electroencephalography (EEG)-based brain–computer interface research. However, there are few published SSVEP datasets for brain–computer interface. In this study, we obtained a new SSVEP dataset based on measurements from 30 participants, performed on 2 days; our dataset complements existing SSVEP datasets: (i) multi-band SSVEP datasets are provided, and all 3 possible frequency bands (low, middle, and high) were used for SSVEP stimulation; (ii) multi-day datasets are included; and (iii) the EEG datasets include simultaneously obtained physiological measurements, such as respiration, electrocardiography, electromyography, and head motion (accelerator). FINDINGS: To validate our dataset, we estimated the spectral powers and classification performance for the EEG (SSVEP) datasets and created an example plot to visualize the physiological time-series data. Strong SSVEP responses were observed at stimulation frequencies, and the mean classification performance of the middle frequency band was significantly higher than the low- and high-frequency bands. Other physiological data also showed reasonable results. CONCLUSIONS: Our multi-band, multi-day SSVEP datasets can be used to optimize stimulation frequencies because they enable simultaneous investigation of the characteristics of the SSVEPs evoked in each of the 3 frequency bands, and solve session-to-session (day-to-day) transfer problems by enabling investigation of the non-stationarity of SSVEPs measured on different days. Additionally, auxiliary physiological data can be used to explore the relationship between SSVEP characteristics and physiological conditions, providing useful information for optimizing experimental paradigms to achieve high performance. Oxford University Press 2019-11-25 /pmc/articles/PMC6876666/ /pubmed/31765472 http://dx.doi.org/10.1093/gigascience/giz133 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Data Note
Choi, Ga-Young
Han, Chang-Hee
Jung, Young-Jin
Hwang, Han-Jeong
A multi-day and multi-band dataset for a steady-state visual-evoked potential–based brain-computer interface
title A multi-day and multi-band dataset for a steady-state visual-evoked potential–based brain-computer interface
title_full A multi-day and multi-band dataset for a steady-state visual-evoked potential–based brain-computer interface
title_fullStr A multi-day and multi-band dataset for a steady-state visual-evoked potential–based brain-computer interface
title_full_unstemmed A multi-day and multi-band dataset for a steady-state visual-evoked potential–based brain-computer interface
title_short A multi-day and multi-band dataset for a steady-state visual-evoked potential–based brain-computer interface
title_sort multi-day and multi-band dataset for a steady-state visual-evoked potential–based brain-computer interface
topic Data Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6876666/
https://www.ncbi.nlm.nih.gov/pubmed/31765472
http://dx.doi.org/10.1093/gigascience/giz133
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