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156 results for “brain interfacing”
Data from: Using adversarial networks to extend brain computer interface decoding accuracy over time
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Data from: Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces
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Data from: The impact of task context on predicting finger movements in a brain-machine interface
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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>
Raw data of manuscript "Tapered fibertrodes for opto-electrical neural interfacing in small brain volumes with reduced artefacts"
<p>This dataset contains the raw data for the paper titled "Tapered fibertrodes for opto-electrical neural interfacing in small brain volumes with reduced artefacts".</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
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Beamforming in Noninvasive Brain–Computer Interfaces Dataset
<p>This is the dataset of 10 motor imagery subjects upon which the paper cited here is written. It is saved in the EEGLAB .set format with the digitized electrode positions included. The only issue is that the names for channels 65-128 are missing, and the head model that corresponds to these electrode locations is also poorly specified (see field Headmodel of the EEG struct). When using this data please cite:</p> <p>Grosse-Wentrup, Moritz, et al. "Beamforming in noninvasive brain–computer interfaces." <em>IEEE Transactions on Biomedical Engineering</em> 56.4 (2009): 1209-1219.</p> <p>DOI: <a href="https://doi.org/10.1109/TBME.2008.2009768">10.1109/TBME.2008.2009768</a></p>
Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC with and without adaptive calibration using Riemannian geometry. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02103098">https://hal.archives-ouvertes.fr/hal-02103098</a>. Data were recorded during an experiment taking place in the GIPSA-lab, Grenoble, France, in 2013 (Congedo, 2013). Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA</a>. The ID of this dataset is<em> BI.EEG.2013-GIPSA</em>.</p> <p> </p> <p><strong>Full description of the experiment and dataset:</strong> <a href="https://hal.archives-ouvertes.fr/hal-02103098">https://hal.archives-ouvertes.fr/hal-02103098</a></p> <p> </p> <p><strong><em>Principal Investigator</em>:</strong> B.Sc. Erwan Vaineau, Ph.D. Alexandre Barachant</p> <p> </p> <p><strong><em>Technical Supervisors</em>: </strong>Eng. Anton Andreev, Eng. Pedro. L. C. Rodrigues, Eng. Grégoire Cattan</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>BI.EEG.2013-GIPSA</em></p>
Effects of a Vibro-Tactile P300 Based Brain-Computer Interface on the Coma Recovery Scale-Revised in Patients With Disorders of Consciousness
<p>Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor and cognitive disabilities. Recent research has shown that non-invasive brain-computer interface (BCI) technology could help assess these patients’ cognitive functions and command following abilities. 20 DOC patients participated in the study and performed 10 vibro-tactile P300 BCI sessions over 10 days with 8–12 runs each day. Vibrotactile tactors were placed on the each patient’s left and right wrists and one foot. Patients were instructed, via earbuds, to concentrate and silently count vibrotactile pulses on either their left or right wrist that presented a target stimulus and to ignore the others. Changes of the BCI classification accuracy were investigated over the 10 days. In addition, the Coma Recovery Scale-Revised (CRS-R) score was measured before and after the 10 vibro-tactile P300 sessions. In the first run, 10 patients had a classification accuracy above chance level (>12.5%). In the best run, every patient reached an accuracy ≥60%. The grand average accuracy in the first session for all patients was 40%. In the best session, the grand average accuracy was 88% and the median accuracy across all sessions was 21%. The CRS-R scores compared before and after 10 VT3 sessions for all 20 patients, are showing significant improvement (<em>p</em> = 0.024). Twelve of the twenty patients showed an improvement of 1 to 7 points in the CRS-R score after the VT3 BCI sessions (mean: 2.6). Six patients did not show a change of the CRS-R and two patients showed a decline in the score by 1 point. Every patient achieved at least 60% accuracy at least once, which indicates successful command following. This shows the importance of repeated measures when DOC patients are assessed. The improvement of the CRS-R score after the 10 VT3 sessions is an important issue for future experiments to test the possible therapeutic applications of vibro-tactile and related BCIs with a larger patient group.</p>
Data and software for "Mental Tasks for Controlling Cursor Movements in a Brain Computer Interface"
<p>All data for the experimental subjects on the examined mental tasks is contained in the "BCI_IFS_RECORDING_2012MARCH~APRIL_GroupIII" Zip folder.</p> <p>The software that was used to process the data is contained in the "IETE3.2_CROSS_VALID_AUTO" Zip folder.</p>
Microelectrode Brain-Machine Interface for Individuals With Tetraplegia
ClinicalTrials.gov study NCT01364480. IPD Sharing: YES. Countries: 1. Publications: 7.
Non-Invasive Brain-Computer Interface Combined With Transcranial Electrical Stimulation for Peripheral Facial PalsyStimulation in the Treatment of Peripheral Facial Palsy
ClinicalTrials.gov study NCT07327710. IPD Sharing: NO. Countries: 1. Publications: 7.
ECoG Direct Brain Interface for Individuals With Upper Limb Paralysis
ClinicalTrials.gov study NCT01393444. IPD Sharing: YES. Countries: 1. Publications: 1.
A P300 Brain Computer Interface Keyboard to Control Assistive Technology For Use by People With Amyotrophic Lateral Sclerosis
ClinicalTrials.gov study NCT01119001. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Brain Machine Interface Control of an Robotic Exoskeleton in Training Upper Extremity Functions in Stroke
ClinicalTrials.gov study NCT01948739. IPD Sharing: NO. Countries: 1. Publications: 5.
Validation of a Brain-Computer Interface for Stroke Neurological Upper Limb Rehabilitation
ClinicalTrials.gov study NCT04724824. IPD Sharing: YES. Countries: 1. Publications: 2.
Stroke Rehabilitation Using Brain-Computer Interface (BCI) Technology
ClinicalTrials.gov study NCT04141774. IPD Sharing: NO. Countries: 1. Publications: 5.
Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface
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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.