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An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons
This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification, transfer learning, EEG preprocessing, blink detec...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712810/ https://www.ncbi.nlm.nih.gov/pubmed/29226211 http://dx.doi.org/10.1016/j.dib.2017.11.032 |
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author | Robbins, Kay Su, Kyung-min Hairston, W. David |
author_facet | Robbins, Kay Su, Kyung-min Hairston, W. David |
author_sort | Robbins, Kay |
collection | PubMed |
description | This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification, transfer learning, EEG preprocessing, blink detection, and automated annotation algorithms. We are releasing the data in three formats to enable benchmarking of EEG algorithms in many areas. The data was acquired using a Biosemi Active 2 EEG headset and includes 64 channels of EEG, 4 channels of EOG (electrooculogram), and 2 mastoid reference channels. |
format | Online Article Text |
id | pubmed-5712810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-57128102017-12-08 An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons Robbins, Kay Su, Kyung-min Hairston, W. David Data Brief Neuroscience This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification, transfer learning, EEG preprocessing, blink detection, and automated annotation algorithms. We are releasing the data in three formats to enable benchmarking of EEG algorithms in many areas. The data was acquired using a Biosemi Active 2 EEG headset and includes 64 channels of EEG, 4 channels of EOG (electrooculogram), and 2 mastoid reference channels. Elsevier 2017-11-13 /pmc/articles/PMC5712810/ /pubmed/29226211 http://dx.doi.org/10.1016/j.dib.2017.11.032 Text en © 2017 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Neuroscience Robbins, Kay Su, Kyung-min Hairston, W. David An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons |
title | An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons |
title_full | An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons |
title_fullStr | An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons |
title_full_unstemmed | An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons |
title_short | An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons |
title_sort | 18-subject eeg data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712810/ https://www.ncbi.nlm.nih.gov/pubmed/29226211 http://dx.doi.org/10.1016/j.dib.2017.11.032 |
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