Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
82
datasets available to search
ShareScore release 0.7.1
Dataset results
82 results for “brain-computer interface”
A dataset recorded during development of an affective brain-computer music interface: calibration session
Open the record for dataset details and reuse information.
A dataset recorded during development of an affective brain-computer music interface: testing session
Open the record for dataset details and reuse information.
A dataset recorded during development of an affective brain-computer music interface: training sessions
Open the record for dataset details and reuse information.
Graphics for implanted brain-computer interfaces for communication and sensorimotor control applications.
<p>Updated information from Nature Reviews Bioengineering, doi: 10.1038/s44222-024-00239-5. Current as of 27 September 2024. Please reference the original publication if using these graphics. As the field moves into the "Translational Era", the original publication reviews the clinical trials up to December 2023. </p>
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>
Data for: Adaptive P300-Based Brain-Computer Interface for Attention Training
<p>The dataset contains EEG and behavioral data of 47 participants who completed 9 runs (i.e. copy-spelled 9 words) in a P300 speller task, as well as a random dot motion (RDM) task and questionnaires in a single experimental session. Details of the experimental protocol can be found here:</p> <p>Noble SC, Woods E, Ward T, Ringwood JV. “Adaptive P300-Based Brain-Computer Interface for Attention Training: Protocol for a Randomized Controlled Trial.” <em>JMIR Res Protoc</em> 2023, 12:e46135, doi: <a href="https://doi.org/10.2196/46135">10.2196/46135</a></p> <p>A journal article describing the results of the study can be found here:<br><br>Noble SC, Woods E, Ward T, Ringwood JV. “Accelerating P300-Based Neurofeedback Training for Attention Enhancement Using Iterative Learning Control: A Randomised Controlled Trial.” <em>J Neural Eng</em> 2024, 21(2), doi: <a href="https://doi.org/10.1088/1741-2552/ad2c9e" target="_blank" rel="noopener">10.1088/1741-2552/ad2c9e</a></p> <p>Please cite the results paper when using the data.</p> <p>Each participant folder contains:</p> <ul> <li>[xxx]-raw.[xxx] – unprocessed EEG signals (<strong>in</strong> <strong>mV</strong>) from 32 electrodes for all 9 P300 speller runs in Openvibe (.ov) and Matlab (.mat) file formats, see details of the runs below</li> <li>[xxx]-processed.[xxx] – contains 3 xDAWN components extracted by the xDAWN spatial filter according to the weights in “spatial-filter.cfg”</li> <li>classifier.cfg - LDA classifier weights</li> <li>spatial-filter.cfg - xDAWN spatial filter weights</li> <li>log.txt - contains the group assignment, start and end time of the experiment, and performance in the P300 speller and RDM tasks</li> </ul> <p>The file “Subject Information.csv” contains the age and gender of all participants.</p> <p>The file “Questionnaire scores.csv” contains the responses to the questionnaire described in the experimental protocol and the NASA Task Load Index (TLX) for all participants.</p> <p>The .ov and .mat files contain data from the following runs:</p> <table> <tbody> <tr> <th>Filename</th> <th>Word to be copy-spelled</th> <th>Number of flashes per row and column</th> <th>Feedback given to participant</th> </tr> </tbody> <tbody> <tr> <td>calibration-signal1</td> <td>THE</td> <td>12</td> <td>no</td> </tr> <tr> <td>calibration-signal2</td> <td>QUICK</td> <td>12</td> <td>no</td> </tr> <tr> <td>calibration-signals</td> <td>Concatenation of calibration-signal1 and calibration-signal2</td> </tr> <tr> <td>eval</td> <td>DOG</td> <td>12</td> <td>yes</td> </tr> <tr> <td>training-run-1</td> <td>BEAUTIFUL</td> <td>10</td> <td>yes</td> </tr> <tr> <td>training-run-2 to training-run-5</td> <td>BEAUTIFUL</td> <td>varying</td> <td>yes</td> </tr> <tr> <td>post-training-run</td> <td>DANCE</td> <td>12</td> <td>yes</td> </tr> </tbody> </table> <p> </p> <p>This research is supported by the Irish Research Council under project ID GOIPG/2020/692 and Science Foundation Ireland under grant number 12/RC/2289_P2.</p>
A dataset recorded during development of a tempo-based brain-computer music interface
Open the record for dataset details and reuse information.
Raw EEG Data for: Learning from Label Proportions in Brain-Computer Interfaces
<p>If you prefer to use the preprocessed and epoched data, please refer to: https://zenodo.org/record/192684</p> <p>Note that this repository ontains only the visual paradigm with the N=13 subjects recorded at 31 EEG channels, as described in the above link. We copied the relevant section of the description below:</p> <blockquote> <p>This data repository contains raw EEG of an EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects.</p> <p>The dataset is used and described in the following journal article:</p> <p><em>Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., & Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.</em></p> <p><strong>Please cite the above article when using the data.</strong></p> <p>The data set with N=13 subjects is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.</p> <p>In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:</p> <p><em>Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., & Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.</em></p> </blockquote> <p>The data was recorded with BrainVision recorder. A new file was recorded for every group of 7 characters. The .eeg file contains the RAW EEG data in the format as described in the .vhdr file. Events / stimuli markers are provided in the .vmrk files. Note that there is a wrapper available to use this data in MOABB here: TODO INSERT LINK</p> <p>The subjects had the task to spell a specific sentence with 63 letters. In the online experiment, this was repeated 3 times and each time the online unsupervised classifier was reset at the start of the sentence.</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>
Physiological Signals During Motor Imagery Brain-Computer Interface Training Using Virtual Reality and Haptics
<p><strong>Participant demographics:</strong></p> <p>The sample is consisted by 20 healthy volunteers with a mean age of 24.79 years (SD = 3.54 years). The cohort was 68% male and 32% female. In terms of education, 16% had attended only high school, while 32% had a bachelor's degree, 42% a master's degree, and 11% a doctorate. All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki.</p> <p><strong>Experiment Description:</strong></p> <p>The experiment consisted in having the subjects perform motor imagery of a bimanual rowing task with two individual paddles, one in each hand, under five experimental conditions. Four of these conditions used NeuRow (<a href="https://link.springer.com/chapter/10.1007/978-3-030-27950-9_1"><strong>Vourvopoulos et al. (2016-2019</strong>))</a>—a VR environment that renders virtual arms from a first-person perspective—while the other conditions used abstract feedback based on the BCI-Graz paradigm<a href="https://ieeexplore.ieee.org/abstract/document/1214714"> (<strong>Pfurtscheller et al. (2003))</strong></a>. All six conditions and their acronyms are described below:</p> <ol> <li><strong>Motor Imagery(MI)</strong>: The standard motor imagery training, with a fixation cross and directional arrows on a black background guiding the subjects through the experiment.</li> <li><strong>Motor Imagery/Motor Observation (MIMO):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor.</li> <li><strong>Motor Imagery/Motor Observation with Haptics (MIMOHP): </strong>A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD (MIMOVR):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD and Haptics (MIMOVRHP):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Execution (ME):</strong> A fixation cross and directional arrows were displayed on a black background through a monitor (same as in MI), and guided the subjects through the experiment by having them tap their fingers accordingly. Data from this condition was available only after S07, so only 10 subjects<br> have performed ME.</li> </ol> <p>Finally, this experiment followed a within-subject design, in a randomized order of the conditions to minimize any order effects, while MI and ME conditions acted as control.</p> <p><strong>Equipment:</strong></p> <p>A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active electrodes(+3 ACC) with a sampling rate of 500Hz. In addition, <strong>ECG, PPG</strong> and <strong>Respiration</strong> signals have been recorded synchronously in a bipolar montage, and connected to the EEG amplifier’s AUX input through the Brain Products BIP2AUX adapter.</p> <p>Visual feedback was provided through a monitor in all conditions except in MIMOVR and MIMOVRHP, in which an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA) was used instead. Haptic feedback was provided through the Oculus Rift hand controllers.<br> </p> <p><strong>Channel Indices:</strong></p> <p><strong>EEG</strong>: 1-32<br> <strong>PPG</strong> (AUX1): 33<br> <strong>Resp</strong>. (AUX2): 34<br> <strong>ECG</strong> (AUX3): 35<br> <strong>ACC</strong>: 36-38</p> <p> </p> <p><strong>Event codes:</strong></p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>S01</td> <td>Experiment Start</td> </tr> <tr> <td>S02</td> <td>Baseline Start</td> </tr> <tr> <td>S03</td> <td>Baseline Stop</td> </tr> <tr> <td>S04</td> <td>Start Of Trial</td> </tr> <tr> <td>S05</td> <td>Cross On Screen</td> </tr> <tr> <td>S07</td> <td>class1, Left hand </td> </tr> <tr> <td>S08</td> <td>class2, Right hand </td> </tr> <tr> <td>S09</td> <td>Feedback Continuous</td> </tr> <tr> <td>S10</td> <td>End of Trial</td> </tr> <tr> <td>S11</td> <td>End Of Session</td> </tr> <tr> <td>S12</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p> </p> <p><strong>Directory tree:</strong></p> <p>ROOT<br> |<br> +--- USER #<br> | +---SESSION #<br> | | +---TASK #<br> | | | +---MI<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMO<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOHP<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOVR<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOHPVR<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---ME<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk</p> <p> </p> <p><strong>Note: </strong>The first three datasets are from pilot sessions: sub-p01 to p03. From sub-01 to 19, subjects 10 and 11 have been removed due to the lack of markers. Subject sub-13, task MIMOVRHP is missing.</p> <p> </p>
Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregives and researchers
<p>Nine animation videos used in the questionnaire described in the articles "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome</strong>" (<a href="https://doi.org/10.1177%2F1545968321989331">https://doi.org/10.1177/1545968321989331</a>) and "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers</strong>" (<a href="https://doi.org/10.1080/17483107.2021.1958932">https://doi.org/10.1080/17483107.2021.1958932</a>). <em>Video animations were designed and produced by Merel Horsmeier.</em></p>
Hyperscanning brain-computer interface based on synchronous and asynchronous interindividual SSVEP signals
<div> <p>Here we provide hyperscanning EEG (electroencephalogram) data recorded during BCI (brain-computer interface) control. The BCI was intended for the decoding of brain synchrony during visual stimulation, specifically the stimuli flickered at two different fequencies.<br>Each of seven pairs of participants performed more than 100 trials, which included 5s visual stimulation of synchronous or asynchronous flicker. See the PDF file for detailed description.</p> </div>
Brain Invaders Solo versus Collaboration: Multi-User P300-based Brain-Computer Interface Dataset (bi2014b)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 38 subjects playing in pair to the multi-user version of a visual P300-based Brain-Computer Interface (BCI) named <em>Brain </em><em>Invaders </em>(Congedo et al., 2011). The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit a P300 response, an evoked-potential appearing about 300ms after stimulation onset. EEG data were recorded using 32 active wet electrodes per subjects (total: 64 electrodes) during three randomized conditions (Solo1, Solo2, Collaboration). The experiment took place at GIPSA-lab, Grenoble, France, in 2014. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02173958">https://hal.archives-ouvertes.fr/hal-02173958</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2014b-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2014b-GIPSA</a>. The ID of this dataset is <em>bi2014b.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02173958">https://hal.archives-ouvertes.fr/hal-02173958</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Ekaterina Ostaschenko</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2014b</em></p>
Brain Invaders Cooperative versus Competitive: Multi-User P300-based Brain-Computer Interface Dataset (bi2015b)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 44 subjects playing in pair to the multi-user version of a visual P300 Brain-Computer Interface (BCI) named <em>Brain </em><em>Invaders</em>. The interface uses the oddball paradigm on a grid of 36 symbols (1 or 2 Target, 35 or 34 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 32 active wet electrodes per subjects (total: 64 electrodes) during four randomised conditions (Cooperation 1-Target, Cooperation 2-Targets, Competition 1-Target, Competition 2-Targets). The experiment took place at GIPSA-lab, Grenoble, France, in 2015. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02173913">https://hal.archives-ouvertes.fr/hal-02173913</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA</a>. The ID of this dataset is <em>bi2015b.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02173913">https://hal.archives-ouvertes.fr/hal-02173913</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Martine Cederhout</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2015b</em></p>
Data from: A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis
Open the record for dataset details and reuse information.
Data from: Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces
Open the record for dataset details and reuse information.
EEG Data for: "Learning from Label Proportions in Brain-Computer Interfaces"
<p>Data about two experiments is contained in this repository. An EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects was conducted in addition to a smaller study with N=5 subjects performing both an auditory and a visual ERP paradigm. </p> <p>The dataset is used and described in the following journal article:</p> <p><em>Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., & Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.</em></p> <p><strong>Please cite the above article when using the data.</strong></p> <p>The larger data set with N=13 is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.</p> <p>In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:</p> <p><em>Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., & Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.</em></p> <p>The following files are available:</p> <p>description.pdf: <strong>Full description of the dataset</strong><br> offline_auditory.zip: <strong>Data from the auditory offline study with N=5 subjects</strong><br> offline_visual.zip: <strong>Data from the visual offline study with N=5 subjects</strong><br> online_study_1-7.zip: <strong>Data from the online study for subjects 1-7</strong><br> online_study_8-13.zip: <strong>Data from the online study for subjects 8-13</strong><br> sequence.mat: <strong>Sequence data necessary for applying LLP to the online study. It is the same for all subjects</strong></p> <p>We will create a git repository with example code soon.</p>
EEG Data for: "Composing only by thought: novel application of the P300 brain-computer interface"
<p>EEG Data for: "Composing only by thought: novel application of the P300 brain-computer interface". See the file "Information about the Dataset.pdf" for further information.</p>
Dataset used in "Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations" 2018, PLOS ONE
<p>This file contains the segmented EEG data in offline and online conditions. Please read the readme.doc file for the detailed explanation.</p>
Eight Reasons to Prioritize Brain-Computer Interface Cybersecurity
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.