A large EEG database with users’ profile information for motor imagery brain-computer interface research
Abstract We present and share a large database containing electroencephalographic signals from 87 human participants, collected during a single day of brain-computer interface (BCI) experiments, organized into 3 datasets (A, B, and C) that were all recorded using the same protocol: right and left ha...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02445-z |
| _version_ | 1797578243390308352 |
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| author | Pauline Dreyer Aline Roc Léa Pillette Sébastien Rimbert Fabien Lotte |
| author_facet | Pauline Dreyer Aline Roc Léa Pillette Sébastien Rimbert Fabien Lotte |
| author_sort | Pauline Dreyer |
| collection | DOAJ |
| description | Abstract We present and share a large database containing electroencephalographic signals from 87 human participants, collected during a single day of brain-computer interface (BCI) experiments, organized into 3 datasets (A, B, and C) that were all recorded using the same protocol: right and left hand motor imagery (MI). Each session contains 240 trials (120 per class), which represents more than 20,800 trials, or approximately 70 hours of recording time. It includes the performance of the associated BCI users, detailed information about the demographics, personality profile as well as some cognitive traits and the experimental instructions and codes (executed in the open-source platform OpenViBE). Such database could prove useful for various studies, including but not limited to: (1) studying the relationships between BCI users’ profiles and their BCI performances, (2) studying how EEG signals properties varies for different users’ profiles and MI tasks, (3) using the large number of participants to design cross-user BCI machine learning algorithms or (4) incorporating users’ profile information into the design of EEG signal classification algorithms. |
| first_indexed | 2024-03-10T22:19:15Z |
| format | Article |
| id | doaj.art-4e7b15fb947542ebbd95117373982f21 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-10T22:19:15Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-4e7b15fb947542ebbd95117373982f212023-11-19T12:19:53ZengNature PortfolioScientific Data2052-44632023-09-0110111410.1038/s41597-023-02445-zA large EEG database with users’ profile information for motor imagery brain-computer interface researchPauline Dreyer0Aline Roc1Léa Pillette2Sébastien Rimbert3Fabien Lotte4Centre Inria de l’université de BordeauxCentre Inria de l’université de BordeauxInria de l’Université de Rennes, CNRS, IRISACentre Inria de l’université de BordeauxCentre Inria de l’université de BordeauxAbstract We present and share a large database containing electroencephalographic signals from 87 human participants, collected during a single day of brain-computer interface (BCI) experiments, organized into 3 datasets (A, B, and C) that were all recorded using the same protocol: right and left hand motor imagery (MI). Each session contains 240 trials (120 per class), which represents more than 20,800 trials, or approximately 70 hours of recording time. It includes the performance of the associated BCI users, detailed information about the demographics, personality profile as well as some cognitive traits and the experimental instructions and codes (executed in the open-source platform OpenViBE). Such database could prove useful for various studies, including but not limited to: (1) studying the relationships between BCI users’ profiles and their BCI performances, (2) studying how EEG signals properties varies for different users’ profiles and MI tasks, (3) using the large number of participants to design cross-user BCI machine learning algorithms or (4) incorporating users’ profile information into the design of EEG signal classification algorithms.https://doi.org/10.1038/s41597-023-02445-z |
| spellingShingle | Pauline Dreyer Aline Roc Léa Pillette Sébastien Rimbert Fabien Lotte A large EEG database with users’ profile information for motor imagery brain-computer interface research Scientific Data |
| title | A large EEG database with users’ profile information for motor imagery brain-computer interface research |
| title_full | A large EEG database with users’ profile information for motor imagery brain-computer interface research |
| title_fullStr | A large EEG database with users’ profile information for motor imagery brain-computer interface research |
| title_full_unstemmed | A large EEG database with users’ profile information for motor imagery brain-computer interface research |
| title_short | A large EEG database with users’ profile information for motor imagery brain-computer interface research |
| title_sort | large eeg database with users profile information for motor imagery brain computer interface research |
| url | https://doi.org/10.1038/s41597-023-02445-z |
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