A large EEG database with users' profile information for motor imagery Brain-Computer Interface research

<p><em><strong>Context </strong></em>:&nbsp;<br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user&rsquo;s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users&#39; profiles and their BCI performances, 2) studying how EEG signals properties varies for different users&#39; profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users&#39; profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters&#39; and users&#39; gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette &amp; al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users&#39; online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch &amp; al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) &amp; OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant&#39;s folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em>&nbsp;</em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users&rsquo; profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> &nbsp;</p>