ArticleiScience2022
Learning to control a BMI-driven wheelchair for people with severe tetraplegia.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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Who cites it
19 citing papers in PubMed, 45 citations in OpenAlex.
- Highly stretchable, hair-compatible multichannel neural interface with time-stable hydrogel for high-fidelity long-term EEG sensing.Science advances · 2026Article
- Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.Nature communications · 2026Article
- Adaptive Neural Reorganization Enables Real-Time Finger-Level Robotic Control in BCI-Naïve Stroke Survivors.bioRxiv : the preprint server for biology · 2026Article
- Investigating the neurovascular coupling across multiple motor execution and imagery conditions: a whole-brain EEG-informed fMRI analysis.NeuroImage · 2025Article
- EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.Nature communications · 2025Article
- Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Master classes of the tenth international brain-computer interface meeting: showcasing the research of BCI trainees.Journal of neural engineering · 2025Article
- Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025Review
- Investigating singing imagery as an additional or alternative control task for EEG-based Brain-Computer Interfaces.Frontiers in human neuroscience · 2025Article
- A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.Scientific data · 2024Article
- Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time.bioRxiv : the preprint server for biology · 2024Article
- Smart Nursing Wheelchairs: A New Trend in Assisted Care and the Future of Multifunctional Integration.Biomimetics (Basel, Switzerland) · 2024Review
- Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention.Nature communications · 2024Article
- On the role of generative artificial intelligence in the development of brain-computer interfaces.BMC biomedical engineering · 2024Review
- Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.PNAS nexus · 2024Article
- Transfer learning promotes acquisition of individual BCI skills.PNAS nexus · 2024Article
- Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.Frontiers in neuroscience · 2024Article
- Editorial: Neurotechnologies and brain-computer interaction for neurorehabilitation.Frontiers in neuroergonomics · 2023Article
- Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time.Brain stimulationArticle
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.
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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.