ArticleFrontiers in integrative neuroscience2016
Forward Prediction in the Posterior Parietal Cortex and Dynamic Brain-Machine Interface.
Article in Frontiers in integrative neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Mechanisms of Visuomotor Interception.Brain sciences · 2026Review
- Inferring Arm Movement Direction from EEG Signals Using Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Neural geometry from mixed sensorimotor selectivity for predictive sensorimotor control.eLife · 2025Article
- Similar neural states, but dissimilar decoding patterns for motor control in parietal cortex.Network neuroscience (Cambridge, Mass.) · 2024Article
- From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control.Neuroscience bulletin · 2022Review
- Efference copy in kinesthetic perception: a copy of what is it?Journal of neurophysiology · 2021Review
- Article
- Targeting Sensory and Motor Integration for Recovery of Movement After CNS Injury.Frontiers in neuroscience · 2021Review
- Next-Generation Prosthetic Hand: from Biomimetic to Biorealistic.Research (Washington, D.C.) · 2021Article
- Interfacing to the brain's motor decisions.Journal of neurophysiology · 2017Review
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Authors and funding
1 author.
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Abstract
While remarkable progress has been made in brain-machine interfaces (BMIs) over the past two decades, it is still difficult to utilize neural signals to drive artificial actuators to produce predictive movements in response to dynamic stimuli. In contrast to naturalistic limb movements largely based on forward planning, brain-controlled neuroprosthetics mainly rely on feedback without prior trajectory formation. As an important sensorimotor interface integrating multisensory inputs and efference copy, the posterior parietal cortex (PPC) might play a proactive role in predictive motor control. Here it is proposed that predictive neural activity in PPC could be decoded to provide prosthetic control signals for guiding BMI systems in dynamic environments.
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