ArticleScientific reports2016
Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 50 citations in OpenAlex.
- Specifications and functional impact of a self-triggered grasp neuroprosthesis developed to restore prehension in hemiparetic post-stroke subjects.Biomedical engineering online · 2024Trial
- BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial.Neurology · 2020Trial
- MEG-BMI to Control Phantom Limb Pain.Neurologia medico-chirurgica · 2018Trial
- Induced sensorimotor brain plasticity controls pain in phantom limb patients.Nature communications · 2016Trial
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Assistive Technologies for Individuals with a Disability from a Neurological Condition: A Narrative Review on the Multimodal Integration.Healthcare (Basel, Switzerland) · 2025Review
- Mapping and decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG.Human brain mapping · 2023Article
- Lower Limb Exoskeleton Sensors: State-of-the-Art.Sensors (Basel, Switzerland) · 2022Review
- Progress in Brain Computer Interface: Challenges and Opportunities.Frontiers in systems neuroscience · 2021Review
- A Roadmap Towards Standards for Neurally Controlled End Effectors.IEEE open journal of engineering in medicine and biology · 2021Article
- Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics.Frontiers in neuroscience · 2020Article
- Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury.Scientific reports · 2019Article
- Integrated Giant Magnetoresistance Technology for Approachable Weak Biomagnetic Signal Detections.Sensors (Basel, Switzerland) · 2018Review
- Training in Use of Brain-Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements.Frontiers in neuroscience · 2018Article
- Decoding finger movement in humans using synergy of EEG cortical current signals.Scientific reports · 2017Article
- Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback.Frontiers in neurorobotics · 2017Article
- Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.Scientific reports · 2016Article
- Common neural correlates of real and imagined movements contributing to the performance of brain-machine interfaces.Scientific reports · 2016Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuroprosthetic arms might potentially restore motor functions for severely paralysed patients. Invasive measurements of cortical currents using electrocorticography have been widely used for neuroprosthetic control. Moreover, magnetoencephalography (MEG) exhibits characteristic brain signals similar to those of invasively measured signals. However, it remains unclear whether non-invasively measured signals convey enough motor information to control a neuroprosthetic hand, especially for severely paralysed patients whose sensorimotor cortex might be reorganized. We tested an MEG-based neuroprosthetic system to evaluate the accuracy of using cortical currents in the sensorimotor cortex of severely paralysed patients to control a prosthetic hand. The patients attempted to grasp with or open their paralysed hand while the slow components of MEG signals (slow movement fields; SMFs) were recorded. Even without actual movements, the SMFs of all patients indicated characteristic spatiotemporal patterns similar to actual movements, and the SMFs were successfully used to control a neuroprosthetic hand in a closed-loop condition. These results demonstrate that the slow components of MEG signals carry sufficient information to classify movement types. Successful control by paralysed patients suggests the feasibility of using an MEG-based neuroprosthetic hand to predict a patient's ability to control an invasive neuroprosthesis via the same signal sources as the non-invasive method.
Indexed as
Identifiers
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.