ArticleFrontiers in systems neuroscience2015
Advancing brain-machine interfaces: moving beyond linear state space models.
Article in Frontiers in systems neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 16 citations in OpenAlex.
- From trade-offs to translation: An interdisciplinary roadmap for neurotechnology.Science advances · 2026Review
- Multiscale brain modeling: bridging microscopic and macroscopic brain dynamics for clinical and technological applications.Frontiers in cellular neuroscience · 2025Review
- The critical stability task: quantifying sensory-motor control during ongoing movement in nonhuman primates.Journal of neurophysiology · 2018Article
- Cortical Decoding of Individual Finger Group Motions Using ReFIT Kalman Filter.Frontiers in neuroscience · 2018Article
- Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law.Journal of neural engineering · 2017Article
- Spatiotemporal Distribution of Location and Object Effects in Primary Motor Cortex Neurons during Reach-to-Grasp.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2016Article
- Spatiotemporal distribution of location and object effects in the electromyographic activity of upper extremity muscles during reach-to-grasp.Journal of neurophysiology · 2016Article
- A four-dimensional virtual hand brain-machine interface using active dimension selection.Journal of neural engineering · 2016Article
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Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs). Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider (i) the dynamic range and precision of natural movements, (ii) differences between cortical activity and actual body movement, (iii) kinematic and muscular synergies, and (iv) the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance.
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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.