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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...

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Detalles Bibliográficos
Autores principales: Robbins, Kay, Su, Kyung-min, Hairston, W. David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
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.
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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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