ArticleJournal of neural engineering2007
Prediction of upper limb muscle activity from motor cortical discharge during reaching.
Article in Journal of neural engineering, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 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
63 citing papers in PubMed, 123 citations in OpenAlex.
- Insights into motor control: predict muscle activity from upper limb kinematics with LSTM networks.Scientific reports · 2026Article
- Selective direct influence of motor cortex on limb muscle activity during naturalistic climbing in mice.Nature neuroscience · 2025Article
- Regional specialization of movement encoding across the primate sensorimotor cortex.Nature communications · 2025Article
- Less is more: selection from a small set of options improves BCI velocity control.Journal of neural engineering · 2025Article
- Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.Journal of neural engineering · 2025Article
- Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.International journal of neural systems · 2025Article
- Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.bioRxiv : the preprint server for biology · 2024Article
- Less is more: selection from a small set of options improves BCI velocity control.bioRxiv : the preprint server for biology · 2024Article
- Article
- Intracortical brain-computer interfaces in primates: a review and outlook.Biomedical engineering letters · 2023Review
- Article
- The science and engineering behind sensitized brain-controlled bionic hands.Physiological reviews · 2022Review
- The future of upper extremity rehabilitation robotics: research and practice.Muscle & nerve · 2020Review
- The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models.Neural computation · 2020Article
- Neural Representation of Observed, Imagined, and Attempted Grasping Force in Motor Cortex of Individuals with Chronic Tetraplegia.Scientific reports · 2020Article
- Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2019Review
- A muscle-activity-dependent gain between motor cortex and EMG.Journal of neurophysiology · 2019Article
- Neural Networks for Modeling Neural Spiking in S1 Cortex.Frontiers in systems neuroscience · 2019Article
- Article
- Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Movement representation by the motor cortex (M1) has been a theoretical interest for many years, but in the past several years it has become a more practical question, with the advent of the brain-machine interface. An increasing number of groups have demonstrated the ability to predict a variety of kinematic signals on the basis of M1 recordings and to use these predictions to control the movement of a cursor or robotic limb. We, on the other hand, have undertaken the prediction of myoelectric (EMG) signals recorded from various muscles of the arm and hand during button pressing and prehension movements. We have shown that these signals can be predicted with accuracy that is similar to that of kinematic signals, despite their stochastic nature and greater bandwidth. The predictions were made using a subset of 12 or 16 neural signals selected in the order of each signal's unique, output-related information content. The accuracy of the resultant predictions remained stable through a typical experimental session. Accuracy remained above 80% of its initial level for most muscles even across periods as long as two weeks. We are exploring the use of these predictions as control signals for neuromuscular electrical stimulation in quadriplegic patients.
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.