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123
datasets available to search
ShareScore release 0.7.1
Dataset results
123 results for “Brain Computer Interface”
Brain Computer Interface Complete locked-in State Communication
ClinicalTrials.gov study NCT02980380. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
Dopamine and Brain Computer Interface
ClinicalTrials.gov study NCT06729658. IPD Sharing: NO. Countries: 1. Publications: 26.
Effectiveness of a Brain-Computer Interface Based System for Cognitive Enhancement in the Normal Elderly
ClinicalTrials.gov study NCT01661894. IPD Sharing: Not stated. Countries: 1. Publications: 1.
the Effectiveness of Brain-computer Interface-pedaling Training System on the Rehabilitation of Stroke
ClinicalTrials.gov study NCT04612426. IPD Sharing: Not stated. Countries: 1. Publications: 1.
BCI (Brain Computer Interface) Intervention in Autism
ClinicalTrials.gov study NCT02445625. IPD Sharing: Not stated. Countries: 0. Publications: 2.
Moving a Paralyzed Hand Through Use of a Brain-Computer Interface
ClinicalTrials.gov study NCT00242242. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Chronic Stroke Rehabilitation With Contralesional Brain-Computer Interface
ClinicalTrials.gov study NCT03611855. IPD Sharing: YES. Countries: 1. Publications: 5.
Brain-computer Interface Commercial Readiness
ClinicalTrials.gov study NCT06521736. IPD Sharing: YES. Countries: 1. Publications: 4.
Brain-Computer Interface (BCI) Based Intervention for Attention Deficit Hyperactivity Disorder (ADHD)
ClinicalTrials.gov study NCT01344044. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Investigating the Use of a Brain-computer Interface Based on TMS Neurofeedback for Upper Limb Stroke Rehabilitation
ClinicalTrials.gov study NCT06164912. IPD Sharing: NO. Countries: 1. Publications: 3.
Invasive Brain-Computer Interfaces for Attention
ClinicalTrials.gov study NCT06940089. IPD Sharing: YES. Countries: 1. Publications: 5.
Brain-Computer Interface (BCI)-Based Feedback for Chronic Pain Management
ClinicalTrials.gov study NCT03032497. IPD Sharing: NO. Countries: 1. Publications: 3.
Brain Computer Interface for Communication in Ventilated Patients
ClinicalTrials.gov study NCT02791425. IPD Sharing: NO. Countries: 1. Publications: 19.
Optimizing BCI-FIT: Brain Computer Interface - Functional Implementation Toolkit
ClinicalTrials.gov study NCT04468919. IPD Sharing: YES. Countries: 1. Publications: 1.
Brain Computer Interface: Neuroprosthetic Control of a Motorized Exoskeleton
ClinicalTrials.gov study NCT02550522. IPD Sharing: Not stated. Countries: 1. Publications: 7.
The Effectiveness of Rehabilitation Training Based on Brain-computer Interface Technology to Improve the Upper Limb Motor Function of Ischemic Stroke.
ClinicalTrials.gov study NCT04387474. IPD Sharing: NO. Countries: 1. Publications: 1.
Brain Computer Interface (BCI) Technology for Stroke Hand Rehabilitation
ClinicalTrials.gov study NCT01287975. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Data from: Stabilizing brain-computer interfaces through alignment of latent dynamics
Open the record for dataset details and reuse information.
Data from: Optimizing the detection of wakeful and sleep-like states for future electrocorticographic brain computer interface applications
Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoG-based BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging for target users (e.g., individuals with tetraplegia) due to severe motor disability. In this study, we present an automated and practical strategy to switch a BCI system on or off based on the cognitive state of the user. Using a logistic regression, we built probabilistic models that utilized sub-dural ECoG signals from humans to estimate in pseudo real-time whether a person is awake or in a sleep-like state, and subsequently, whether to turn a BCI system on or off. Furthermore, we constrained these models to identify the optimal anatomical and spectral parameters for delineating states. Other methods exist to differentiate wake and sleep states using ECoG, but none account for practical requirements of BCI application, such as minimizing the size of an ECoG implant and predicting states in real time. Our results demonstrate that, across 4 individuals, wakeful and sleep-like states can be classified with over 80% accuracy (up to 92%) in pseudo real-time using high gamma (70–110 Hz) band limited power from only 5 electrodes (platinum discs with a diameter of 2.3 mm) located above the precentral and posterior superior temporal gyrus.
Plug and play stability for intracortical brain-computer interfaces: A one-year demonstration of seamless brain-to-text communication
<p>Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. In this study, we propose a method: Continual Online Recalibration with Pseudo-labels (CORP), that enables self-recalibration of communication iBCIs without interrupting the user. We evaluated CORP with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods.</p> <p>This dataset contains 21 sessions of recorded neural activities used for the evaluation. It has been formatted for developing and evaluating machine learning models. There 5 more sessions heldout for a planned iBCI stability competition. They will be released in the future.</p> <p>We also provide a pretrained RNN seed model and a laugnage model to preproduce the results in our paper.</p>
ScienceDex guides
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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.