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.
156
datasets available to search
ShareScore release 0.9.0
Dataset results
156 results for “brain interfacing”
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.
Cortical Recording and Stimulating Array Brain-Machine Interface
ClinicalTrials.gov study NCT01894802. IPD Sharing: YES. Countries: 1. Publications: 10.
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.
EEG Brain-Machine Interface Control of an Upper-Limb Robotic Exoskeleton for Robot-Assisted Rehabilitation After Stroke
ClinicalTrials.gov study NCT05374486. IPD Sharing: NO. Countries: 1. Publications: 5.
Data from: Stabilizing brain-computer interfaces through alignment of latent dynamics
Open the record for dataset details and reuse information.
Data from: Playing 20 Questions with the mind: collaborative problem solving by humans using a brain-to-brain interface
We present, to our knowledge, the first demonstration that a non-invasive brain-to-brain interface (BBI) can be used to allow one human to guess what is on the mind of another human through an interactive question-and-answering paradigm similar to the "20 Questions" game. As in previous non-invasive BBI studies in humans, our interface uses electroencephalography (EEG) to detect specific patterns of brain activity from one participant (the "respondent"), and transcranial magnetic stimulation (TMS) to deliver functionally-relevant information to the brain of a second participant (the "inquirer"). Our results extend previous BBI research by (1) using stimulation of the visual cortex to convey visual stimuli that are privately experienced and consciously perceived by the inquirer; (2) exploiting real-time rather than off-line communication of information from one brain to another; and (3) employing an interactive task, in which the inquirer and respondent must exchange information bi-directionally to collaboratively solve the task. The results demonstrate that using the BBI, ten participants (five inquirer-respondent pairs) can successfully identify a "mystery item" using a true/false question-answering protocol similar to the "20 Questions" game, with high levels of accuracy that are significantly greater than a control condition in which participants were connected through a sham BBI.
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.
Low-complexity Reinforcement Learning Decoders for Autonomous, Scalable, Neuromorphic intra-cortical Brain Machine Interfaces
<p><strong>General Description. </strong>TThis dataset comprises recordings from four BMI (Brain-Machine Interface) experiments conducted on two adult macaques. Three of the experiments involved joystick-controlled tasks, while the fourth was a center-out reaching task. In the center-out task, the macaque was trained to maneuver a joystick-controlled cursor from a central position on a computer screen to one of eight square-shaped target locations. The macaques were able to use a wireless integrated system to control a robotic platform (on which they were seated) enabling independent mobility driven by neuronal activity in their motor cortices. Neural activity was recorded from populations of single neurons via multiple electrode arrays implanted in the arm region of the primary motor cortex. A general overview is provided below:</p> <ol> <li>A titanium head post (Crist Instruments, MD, USA) was surgically affixed before implanting the microelectrode arrays. In NHP-A, four microelectrode arrays with 16 electrodes each were implanted, while NHP-B was implanted with one array containing 100 electrodes in the hand/arm region of the left primary motor cortex.</li> <li>Spike signals were recorded using an in-house 100-channel wireless neural recording system, sampled at 13 kHz. The wide-band signals were band-pass filtered between 300 and 3000 Hz to eliminate low-frequency components. Spike detection thresholds were determined using the formula: Thr = 5σ, where σ = median(|x| / 0.6745), <em>x</em> is the filtered signal, and <em>σ</em> estimates the standard deviation of background noise.</li> </ol> <p>In Experiments 1, 2, and 3, the behavioral task involved controlling the motion of a robotic wheelchair using a three-directional, spring-loaded joystick. These tasks included: a) turning 90° right, b) moving forward by 2 meters, c) turning 90° left, and d) remaining stationary for 5 seconds (stop task). The success rate varied across experiments. Experiment 4 also involved joystick control, but followed a classical center-out reaching paradigm. </p> <p><strong>Dataset Description. </strong>The dataset is organized into folders (labeled as experiment 1, 2, 3, and 4) containing data from both NHP-A and NHP-B. Each folder contains data from separate dates labeled as YYMMDD (at the end of the filename). For experiment 1, data from the following dates are present: 15-10-08, 15-10-12, 15-10-19, 15-10-26, 15-11-02, 15-11-16, 15-11-23, and 15-12-10. For experiment 2, following dates are: 18-02-20, 18-03-06, 18-03-08, 18-03-20, 18-03-26, 18-04-13, 18-04-16, 18-04-23. For experiment 3: 14-08-14, 14-08-18, 14-08-20, 15-10-14. For experiment 4: 18-12-03, 18-12-13, 19-01-07, 19-02-20. File naming conventions across all experiments are as follows</p> <ol> <li>targTest: This corresponds to the direction of the joystick recorded for each trial. (decoded using the decoder)</li> <li>targTrain: Ground truth label, corresponding to the actual direction of the joystick.</li> <li>testSet: Number of spike counts from each channel (used for testing corresponding to all the sessions)</li> <li>trainSet: Number of spike counts from each channel (used for calibration, mostly) </li> </ol> <p><strong>Additional Information.</strong> This dataset is a simplified and curated version designed to reproduce the results presented in the associated paper. Note that Experiments 1 and 3 have partial datasets already publicly available at: <a href="https://osf.io/dce96/" target="_new" rel="noopener">https://osf.io/dce96/</a>. However, those versions are raw and can be processed using the variable descriptions below to extract spike counts with a specified bin width. Each file includes the following fields:</p> <ol> <li> joystick_adfreq: The frequency of operation of the joystick.</li> <li>X_Voltage: The voltage reading corresponding to the x-coordinate (while joystick operation).</li> <li>Y_Voltage: The voltage reading corresponding to the y-coordinate (while joystick operation).</li> <li>Spike_data(Channel Number): The Channel Number corresponding to which the neuronal data is recorded.</li> <li>Spike_data(Cluster): Descripting the cluster on which the channels are placed.</li> <li>Spike_data(Spike Times): The timestamp corresponding to the detection of a spike.</li> <li>Spike_data(Spike Number): The total number of spikes calculated for a particular trial from a particular channel.</li> <li>Spike_data(Mean Spike Waveform): The mean neuronal data (for that trial from a particular channel) describing a spike.</li> <li>Spike_data(Spike Amplitude): The mean spike amplitude of that particular channel.</li> <li>IMETrainingData(SentSignals): The truth labels corresponding to a particular trial.</li> <li>IMETrainingData(Timestamps): Time stamps corresponding to each sent signal (data).</li> <li>IMETrainingData(ReasonFail): String data; Reason if the trial failed.</li> <li>IMETrainingData(TrialOutcomes): Trial results corresponding to successful or unsuccessful!</li> <li>IMETrainingData(StartTime): corresponding to the beginning of each trial.</li> <li>IMETrainingData(EndTime): corresponding to the end of each trial.</li> </ol> <p><strong>Possible use cases. </strong>This dataset is well-suited for designing, training, and evaluating iBMI decoders. It provides a valuable resource for researchers aiming to model sensorimotor cortical spiking, benchmark iBMI decoders under consistent conditions, or explore neuromorphic and reinforcement learning-based approaches to decoder design.</p> <p><strong>Contact Information. </strong>We would be delighted to hear from you if you find this dataset useful—especially if it contributes to a publication. Contact: A. Basu <arinbasu@cityu.edu.hk>; A. Ghosh <aghosh14@illinois.edu>.</p> <p><strong>Citation. </strong>A. Ghosh, S. Shaikh, B. Zhou, P. S. V. Sun, C. Libedinsky, R. So, A. Basu, "Low-complexity Reinforcement Learning Decoders for Autonomous, Scalable, Neuromorphic intra-cortical Brain Machine Interfaces," Neuroelectronics 2025(2):0006, <a href="https://doi.org/10.55092/neuroelectronics20250006">https://doi.org/10.55092/neuroelectronics20250006</a></p>
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>
Data from: Investigating the impact of feedback update interval on the efficacy of restorative brain–computer interfaces
Restorative brain–computer interfaces (BCIs) have been proposed to enhance stroke rehabilitation. Restorative BCIs are able to close the sensorimotor loop by rewarding motor imagery (MI) with sensory feedback. Despite the promising results from early studies, reaching clinically significant outcomes in a timely fashion is yet to be achieved. This lack of efficacy may be due to suboptimal feedback provision. To the best of our knowledge, the optimal feedback update interval (FUI) during MI remains unexplored. There is evidence that sensory feedback disinhibits the motor cortex. Thus, in this study, we explore how shorter than usual FUIs affect behavioural and neurophysiological measures following BCI training for stroke patients using a single-case proof-of-principle study design. The action research arm test was used as the primary behavioural measure and showed a clinically significant increase (36%) over the course of training. The neurophysiological measures including motor evoked potentials and maximum voluntary contraction showed distinctive changes in early and late phases of BCI training. Thus, this preliminary study may pave the way for running larger studies to further investigate the effect of FUI magnitude on the efficacy of restorative BCIs. It may also elucidate the role of early and late phases of motor learning along the course of BCI training.
Figure 2 from: Klein A (2016) Brain Graph Interface. Research Ideas and Outcomes 2: e8817. https://doi.org/10.3897/rio.2.e8817
Figure 2 - Schematic of Mindboggle's graph-based database of anatomical features. Top: different structures derived from brain images: surface patches fragmented by application of the Laplace-Beltrami operator, sulcus folds and subfolds, and structures within a subfold. Bottom: schematic graph diagrams representing the relationships among the nested structures. Bottom right: examples of features as properties of edges (relationships such as Part of, Connected to, Has label) and nodes (geometric, shape, and spectral measures).
Figure 3 from: Klein A (2016) Brain Graph Interface. Research Ideas and Outcomes 2: e8817. https://doi.org/10.3897/rio.2.e8817
Figure 3 - Example of natural morphological variability: left inferior parietal lobule (IPL; figure from [Kiriyama et al. 2009]). (A-D) are MRI data and (E-G) are post-mortem specimens. (A) IPL is highlighted and folds are outlined. (B,E) Typical folding pattern. (C,F) PreSMG pattern: an additional gyrus (ellipse) lies between postCS and SMG. (D,G) PreAG pattern: an additional gyrus (ellipse) lies between SMG and AG. [SMG: supramarginal gyrus; AG: angular gyrus; postCS: postcentral sulcus; IPS: intraparietal sulcus; Sy: Sylvian fissure, STS: superior temporal sulcus; *sulcus intermedius primus] postCS IPS * * Sy STS IPL
Sensorimotor Rhythm Brain-Computer Interface Switch to Operate Assistive Technology
ClinicalTrials.gov study NCT01117727. IPD Sharing: Not stated. Countries: 1. Publications: 0.
P300 Brain Computer Interface Keyboard to Operate Assistive Technology
ClinicalTrials.gov study NCT00860951. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Brain-Computer Interface-Based Attention Training Program Compared With Methylphenidate and Citicoline
ClinicalTrials.gov study NCT07333339. IPD Sharing: NO. Countries: 1. Publications: 0.
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.