ArticleFrontiers in neuroscience2016
What Turns Assistive into Restorative Brain-Machine Interfaces?
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 45 citations in OpenAlex.
- Multi-Channel Neural Interface for Neural Recording and Neuromodulation.Small methods · 2026Review
- Multimodal closed-loop strategies for gait recovery after spinal cord injury and stroke via the integration of robotics and neuromodulation.Frontiers in neuroscience · 2025Review
- Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.Journal of neuroengineering and rehabilitation · 2024Article
- An EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton.Scientific data · 2023Article
- Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton.iScience · 2023Article
- Review of tDCS Configurations for Stimulation of the Lower-Limb Area of Motor Cortex and Cerebellum.Brain sciences · 2022Review
- Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton.Frontiers in bioengineering and biotechnology · 2020Article
- Brain-Machine Neurofeedback: Robotics or Electrical Stimulation?Frontiers in bioengineering and biotechnology · 2020Article
- State-Dependent Gain Modulation of Spinal Motor Output.Frontiers in bioengineering and biotechnology · 2020Article
- Recruitment of Additional Corticospinal Pathways in the Human Brain with State-Dependent Paired Associative Stimulation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
- Reaction Time Predicts Brain-Computer Interface Aptitude.IEEE journal of translational engineering in health and medicine · 2018Article
- Proprioceptive Feedback Facilitates Motor Imagery-Related Operant Learning of Sensorimotor β-Band Modulation.Frontiers in neuroscience · 2017Article
- Application of the Stockwell Transform to Electroencephalographic Signal Analysis during Gait Cycle.Frontiers in neuroscience · 2017Article
- Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis.NeuroImage. Clinical · 2017Article
- Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration.Frontiers in neuroscience · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-machine interfaces (BMI) may support motor impaired patients during activities of daily living by controlling external devices such as prostheses (assistive BMI). Moreover, BMIs are applied in conjunction with robotic orthoses for rehabilitation of lost motor function via neurofeedback training (restorative BMI). Using assistive BMI in a rehabilitation context does not automatically turn them into restorative devices. This perspective article suggests key features of restorative BMI and provides the supporting evidence: In summary, BMI may be referred to as restorative tools when demonstrating subsequently (i) operant learning and progressive evolution of specific brain states/dynamics, (ii) correlated modulations of functional networks related to the therapeutic goal, (iii) subsequent improvement in a specific task, and (iv) an explicit correlation between the modulated brain dynamics and the achieved behavioral gains. Such findings would provide the rationale for translating BMI-based interventions into clinical settings for reinforcement learning and motor rehabilitation following stroke.
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.