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11 results for “EEG Database”
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <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’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' 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.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & 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) & 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'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> </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’ 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> </p>
COG-BCI database: A multi-session and multi-task EEG cognitive dataset for passive brain-computer interfaces
<p>Brain-Computer Interfaces, and especially passive Brain-Computer Interfaces (pBCI), with their ability to estimate and detect mental states, are receiving increasing attention from both the scientific and the research and development communities. Many pBCIs aim to increase the safety of complex work environments such as in the aeronautical domain. Therefore, mental workload, vigilance and decision-making are some of the most commonly examined aspects of cognition within this field of research. A large proportion of pBCIs involve a component of machine learning and signal processing as the data that are collected need to be transformed into a reliable estimate of the users’ current mental state (e.g. mental workload). Improving this component is a major challenge for researchers, requiring large quantities of data. While data sharing is common for the active BCI community, open pBCI datasets are scarcer and generally incomplete with regards to the information they report. This is particularly true for datasets encompassing several tasks or sessions, which are of importance for tackling the challenges of transfer learning. Testing new pipelines, feature extraction algorithms and classifiers are central issues for future advances in research within this domain, as well as for algorithm benchmark and research reproducibility.The COG-BCI database presented here is comprised of the recordings of 29 participants over 3 individual sessions with 4 different tasks designed to elicit different cognitive states. This results in a total of over 100 hours of open electrophysiological (EEG) and electrocardiogram (ECG) data. The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342). The dataset was validated on a subjective, behavioral and physiological level (i.e. cardiac and cerebral activity), to ensure its usefulness to the pBCI community. This body of work represents a large effort to promote the use of pBCIs, as well as the use of open science.</p> <p> </p> <p><strong>The data are in the Brain Imaging Data Structure (BIDS) format. For more information, please read the COG-BCI_info.pdf file.</strong></p> <p><strong>Please note that version 4 corrected an electrode name mismatch, for which we sincerely apologize. The answers to the RSME and KSS questionnaires are provided in two separate .txt files.</strong></p>
ESAA: an EEG-Speech auditory attention detection database
<p>We build a database for AAD research, which consists of competing speech stimuli and associated human neural responses, i.e, electroencephalography (EEG) recordings, namely EEG-Speech AAD (ESAA) database. This is the first AAD database with speech stimuli in a tonal language (Mandarin). Moreover, we develop an AAD baseline as a reference model for decoding which speech stream a listening subject is attending to (speaker attention detection), and a baseline for decoding which spatial locus a listening subject is attending to (speaker locus attention detection) on the ESAA database.</p> <p>We release the source code and the database for use in research purpose.</p> <p>This database consists of response data for 17 normal-hearing subjects (S1-S17). It includes:</p> <p>- 64-channel EEG data: responses to two-speaker speech stimuli<br> - Auditory stimuli data (clean): Chinese short stories narrated by a female and a male professional story teller. <br> - Auditory stimuli data (hrtf): Auditory stimuli after head-related transfer function (HRTF) filtering (simulating sound coming from ± 90 deg).<br> - Preprocessing code<br> - AAD baseline (CNN model)</p>
BIDS CHB-MIT Scalp EEG Database
<p>This dataset is a BIDS-compatible version of the CHB-MIT Scalp EEG Database. It reorganizes the file structure to comply with the BIDS specification. To this effect:</p><ul><li>The data from subject chb21 was moved to sub-01/ses-02.</li><li>Metadata was organized according to BIDS.</li><li>Data in the EEG edf files was modified to keep only the 18 channels from a double banana bipolar montage.</li><li>Annotations were formatted as BIDS-score compatible `tsv` files.</li></ul><h3><strong>Details related to access to the data</strong></h3><h4><strong>License</strong></h4><p>The dataset is released under the <a href="https://physionet.org/content/chbmit/view-license/1.0.0/">Open Data Commons Attribution License v1.0</a>.</p><h4><strong>Contact person</strong></h4><p>The original Physionet CHB-MIT Scalp EEG Database was published by Ali Shoeb. This BIDS-compatible version of the dataset was published by Jonathan Dan.</p><h4><strong>Practical information to access the data</strong></h4><p>The original Physionet CHB-MIT Scalp EEG Database is available on the <a href="https://physionet.org/content/chbmit/1.0.0/">Physionet website</a>.</p><h3><strong>Overview</strong></h3><h4><strong>Project name</strong></h4><p>CHB-MIT Scalp EEG Database</p><h4><br><strong>Year that the project ran</strong></h4><p>2010</p><h4><strong>Brief overview of the tasks in the experiment</strong></h4><p>This database, collected at the Children's Hospital Boston, consists of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure medication in order to characterize their seizures and assess their candidacy for surgical intervention.</p><h4><strong>Description of the contents of the dataset</strong></h4><p>Each folder (sub-01, sub-01, etc.) contains between 9 and 42 continuous .edf files from a single subject. Hardware limitations resulted in gaps between consecutively-numbered .edf files, during which the signals were not recorded; in most cases, the gaps are 10 seconds or less, but occasionally there are much longer gaps. In order to protect the privacy of the subjects, all protected health information (PHI) in the original .edf files has been replaced with surrogate information in the files provided here. Dates in the original .edf files have been replaced by surrogate dates, but the time relationships between the individual files belonging to each case have been preserved. In most cases, the .edf files contain exactly one hour of digitized EEG signals, although those belonging to case sub-10 are two hours long, and those belonging to cases sub-04, sub-06, sub-07, sub-09, and sub-23 are four hours long; occasionally, files in which seizures are recorded are shorter.</p><p>The EEG is recorded at 256 Hz with a 16-bit resolution. The recordings are referenced in a double banana bipolar montage with 18 channels from the 10-20 electrode system.</p><p>The dataset also contains seizure annotations as start and stop times.</p><p>The dataset contains 664 `.edf` recordings. 129 those files that contain one or more seizures. In all, these records include 198 seizures.</p><h3><strong>Methods</strong></h3><h4><strong>Subjects</strong></h4><p>23 pediatric subjects with intractable seizures. (5 males, ages 3–22; and 17 females, ages 1.5–19; 1 n/a)</p><h4><strong>Apparatus</strong></h4><p>Recordings were performed at the Children's Hospital Boston using the International 10-20 system of EEG electrode positions. Signals were sampled at 256 samples per second with 16-bit resolution.</p>
Meta-EEG of Siena Scalp EEG Database v1.0.0
<p>Artificial intelligence (AI) based automated epilepsy diagnosis has aimed to ease the burden of manual detection, prediction, and management of seizure and epilepsy-specific EEG signals for medical specialists. With increasing open-source, raw and large EEG databases, there is a need for data standardization of patient and seizure sensitive AI analysis with reduced redundant information. This work releases a balanced, annotated, fixed time and length meta-data of Siena Scalp EEG database v1.0.0.</p> <p>The work releases patient inter-specific and patient non-specific EEG data extracted using specific time stamps of ictal, pre-ictal, post-ictal and peri-ictal EEG provided in the original database (annotations). Further details of this metadata can be found in the provided csv file (Siena DB timestamp.csv). The released EEG data is available in csv format and class labels are provided in the last row of the csv files. PN00-3 has not been included in this database. </p> <p> </p> <h2><strong>Latest update: </strong>The dataset has been moved to: <a href="https://figshare.com/articles/dataset/Data_of_Meta-EEGs/30000775">Data of Meta-EEGs</a></h2> <h2><strong>Please only download and cite the latest version!</strong></h2>
Database of movement behavior and EEG in virtual audiovisual everyday-life environments for hearing aid research
<p>This database contains movement behavior (head, eye, torso) and EEG signals of 21 young normal-hearing (11 male, 11 female, mean age 25 +/- 3.6 years) and 19 elderly normal-hearing subjects (9 male, 12 female, mean age 69 +/- 5.4 years) measured in virtual audiovisual listening environments in the laboratory. The virtual audiovisual environments that were used are: a living room, a lecture hall, a cafeteria, a street and a train station. The video and audio material for the environments is also available (see Related identifiers). The methods and an analysis of the movement behavior are described in Hendrikse et al. (2019). The supplementary materials to this paper that are published here include plots of the gaze trajectories of the subjects in all environments, plotted separately for the young and elderly subjects so that they can be compared, and histograms of the head-, eye- and torso-rotation for the environments that were not included in the paper.</p>
Tactile Stimulation in a Healthy Mexican Sample: An EEG Database
<p>Tecnologico de Monterrey School of Engineering and Sciences </p> <p>NeuroTechs Research Group</p> <p><strong>Context:</strong> This dataset includes electroencephalographic (EEG) recordings from 34 healthy, young adults in Mexico, collected to study the somatosensory system's responses to a range of tactile stimuli. The study employs innovative NeuroSense tactile stimulators to explore how the brain processes touch sensations when subjected to stimuli such as air, vibration, and caress at four distinct intensity levels.</p> <p><strong>Objective: </strong>The objective of this database is to understand the cortical processing of tactile stimuli including air, vibration and caress using EEG.</p> <p><strong>Main Outcome Measure:</strong> The main outcome measure is the EEG recordings which include the evoked responses of the somatosensory system to each type of stimulus and intensities. These measurements allow for an in-depth analysis of the cortical dynamics involved in processing touch.</p> <p><strong>Limitations</strong>: One limitation of the database is its focus on a relatively small and specific population, which could affect the generalizability of the findings. Additionally, the data is dependent on the accuracy and consistency of the stimulus delivery and EEG recording during the experimental sessions.</p> <p><strong>Generalizability:</strong> While the findings provide significant insights into the neural processing of tactile stimuli within the central nervous system, their generalizability might be limited due to the specialized nature of the stimuli and the controlled experimental conditions. However, the dataset serves as a valuable resource for developing diagnostic and therapeutic strategies for somatosensory impairments and advancing research in neuroscience and somatosensory rehabilitation.</p>
NEED: Neuromed Epilepsy EEG Database. A Large EEG Database of Epilepsy Patients for Research Community.
ClinicalTrials.gov study NCT04647825. IPD Sharing: NO. Countries: 1. Publications: 19.
Meta-EEG of CHB-MIT Scalp EEG Database v1.0.0.0
<p>Artificial intelligence (AI) based automated epilepsy diagnosis has aimed to ease the burden of manual detection, prediction, and management of seizure and epilepsy-specific EEG signals for medical specialists. With increasing open-source, raw, and large EEG datasets, there is a need for data standardization of patient and seizure-sensitive AI analysis with reduced redundant information. This work releases a balanced, annotated, fixed time and length meta-data of CHB-MIT Scalp EEG database v1.0.0.0.</p> <p>The work releases patient-specific (inter and intra) and patient non-specific EEG data extracted using specific time stamps of ictal, pre-ictal, post-ictal, peri-ictal, and non-seizure EEG provided in the original dataset (annotations). Further details of this metadata can be found in the provided csv file (CHB-MIT DB timestamp.csv). The released EEG data is available in csv format and class labels are provided in the last row of the csv files. Data of ch06, ch12, ch23, and ch24 in patient-specific and chb24_11 in patient non-specific have not been included. The importance of peri-ictal EEGs has been elucidated in Handa, P., & Goel, N. (2021). Peri‐ictal and non‐seizure EEG event detection using generated metadata. Expert Systems, e12929.</p> <h2><strong>Latest update: </strong>The dataset has been moved to: <a href="https://figshare.com/articles/dataset/Data_of_Meta-EEGs/30000775">Data of Meta-EEGs</a></h2> <h2><strong>Please only download and cite the latest version!</strong></h2> <p> </p>
EEG-based Brain-computer Interface Database for Motor Rehabilitation
ClinicalTrials.gov study NCT06861517. IPD Sharing: NO. Countries: 1. Publications: 0.
De-identified UNMH EEG Corpus Database Creation With Fully De-identified Clinical Information
ClinicalTrials.gov study NCT05140265. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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