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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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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. |
format | Online Article Text |
id | pubmed-6876666 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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