ArticleScientific data2021
Continuous sensorimotor rhythm based brain computer interface learning in a large population.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 46 citations in OpenAlex.
- Continual-Learning-Enhanced CNN-Transformer Framework for Real-Time Motor-Imagery BCI in Virtual Environments.Bioengineering (Basel, Switzerland) · 2026Article
- A confidence-gated source selection strategy for cross-session transfer in brain-computer interfaces.Frontiers in human neuroscience · 2026Article
- Brain-computer interface control with artificial intelligence copilots.Nature machine intelligence · 2025Article
- [Research on the relationship between resting-state spontaneous electroencephalography and task-evoked electroencephalography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- P300 ERP System Utilizing Wireless Visual Stimulus Presentation Devices.Sensors (Basel, Switzerland) · 2025Article
- Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025Review
- A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.Scientific data · 2024Article
- Thought-Controlled Computer Applications: A Brain-Computer Interface System for Severe Disability Support.Sensors (Basel, Switzerland) · 2024Article
- Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.PNAS nexus · 2024Article
- Review of public motor imagery and execution datasets in brain-computer interfaces.Frontiers in human neuroscience · 2023Review
- Neural correlates of user learning during long-term BCI training for the Cybathlon competition.Journal of neuroengineering and rehabilitation · 2022Article
- Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film.Scientific data · 2022Article
- On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2022Article
- Closed-loop motor imagery EEG simulation for brain-computer interfaces.Frontiers in human neuroscience · 2022Article
- 2020 International brain-computer interface competition: A review.Frontiers in human neuroscience · 2022Review
- Benefits of deep learning classification of continuous noninvasive brain-computer interface control.Journal of neural engineering · 2021Article
- Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation.Frontiers in neuroscience · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Brain computer interfaces (BCIs) are valuable tools that expand the nature of communication through bypassing traditional neuromuscular pathways. The non-invasive, intuitive, and continuous nature of sensorimotor rhythm (SMR) based BCIs enables individuals to control computers, robotic arms, wheel-chairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large and uniform datasets are needed to design, evaluate, and improve the BCI algorithms. In this work, we release a large and longitudinal dataset collected during a study that examined how individuals learn to control SMR-BCIs. The dataset contains over 600 hours of EEG recordings collected during online and continuous BCI control from 62 healthy adults, (mostly) right hand dominant participants, across (up to) 11 training sessions per participant. The data record consists of 598 recording sessions, and over 250,000 trials of 4 different motor-imagery-based BCI tasks. The current dataset presents one of the largest and most complex SMR-BCI datasets publicly available to date and should be useful for the development of improved algorithms for BCI control.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.