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A dataset of EEG signals from a single-channel SSVEP-based brain computer interface
The paper presents a collection of electroencephalography (EEG) data from a portable Steady State Visual Evoked Potentials (SSVEP)-based Brain Computer Interface (BCI). The collection of data was acquired by means of experiments based on repetitive visual stimuli with four different flickering frequ...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890157/ https://www.ncbi.nlm.nih.gov/pubmed/33659590 http://dx.doi.org/10.1016/j.dib.2021.106826 |
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author | Acampora, Giovanni Trinchese, Pasquale Vitiello, Autilia |
author_facet | Acampora, Giovanni Trinchese, Pasquale Vitiello, Autilia |
author_sort | Acampora, Giovanni |
collection | PubMed |
description | The paper presents a collection of electroencephalography (EEG) data from a portable Steady State Visual Evoked Potentials (SSVEP)-based Brain Computer Interface (BCI). The collection of data was acquired by means of experiments based on repetitive visual stimuli with four different flickering frequencies. The main novelty of the proposed data set is related to the usage of a single-channel dry-sensor acquisition device. Different from conventional BCI helmets, this kind of device strongly improves the users’ comfort and, therefore, there is a strong interest in using it to pave the way towards the future generation of Internet of Things (IoT) applications. Consequently, the dataset proposed in this paper aims to act as a key tool to support the research activities in this emerging topic of human-computer interaction. |
format | Online Article Text |
id | pubmed-7890157 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-78901572021-03-02 A dataset of EEG signals from a single-channel SSVEP-based brain computer interface Acampora, Giovanni Trinchese, Pasquale Vitiello, Autilia Data Brief Data Article The paper presents a collection of electroencephalography (EEG) data from a portable Steady State Visual Evoked Potentials (SSVEP)-based Brain Computer Interface (BCI). The collection of data was acquired by means of experiments based on repetitive visual stimuli with four different flickering frequencies. The main novelty of the proposed data set is related to the usage of a single-channel dry-sensor acquisition device. Different from conventional BCI helmets, this kind of device strongly improves the users’ comfort and, therefore, there is a strong interest in using it to pave the way towards the future generation of Internet of Things (IoT) applications. Consequently, the dataset proposed in this paper aims to act as a key tool to support the research activities in this emerging topic of human-computer interaction. Elsevier 2021-02-02 /pmc/articles/PMC7890157/ /pubmed/33659590 http://dx.doi.org/10.1016/j.dib.2021.106826 Text en © 2021 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Data Article Acampora, Giovanni Trinchese, Pasquale Vitiello, Autilia A dataset of EEG signals from a single-channel SSVEP-based brain computer interface |
title | A dataset of EEG signals from a single-channel SSVEP-based brain computer interface |
title_full | A dataset of EEG signals from a single-channel SSVEP-based brain computer interface |
title_fullStr | A dataset of EEG signals from a single-channel SSVEP-based brain computer interface |
title_full_unstemmed | A dataset of EEG signals from a single-channel SSVEP-based brain computer interface |
title_short | A dataset of EEG signals from a single-channel SSVEP-based brain computer interface |
title_sort | dataset of eeg signals from a single-channel ssvep-based brain computer interface |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890157/ https://www.ncbi.nlm.nih.gov/pubmed/33659590 http://dx.doi.org/10.1016/j.dib.2021.106826 |
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