ArticleNeural computation2006
Identification of multiple-input systems with highly coupled inputs: application to EMG prediction from multiple intracortical electrodes.
Article in Neural computation, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- A generalized coherence framework for quantifying input contributions in multi-input systems with correlated or uncorrelated inputs.Biomedical signal processing and control · 2025Article
- Motor cortex retains and reorients neural dynamics during motor imagery.Nature human behaviour · 2024Article
- Hemicraniectomy in Traumatic Brain Injury: A Noninvasive Platform to Investigate High Gamma Activity for Brain Machine Interfaces.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2019Article
- A muscle-activity-dependent gain between motor cortex and EMG.Journal of neurophysiology · 2019Article
- Adaptive neuron-to-EMG decoder training for FES neuroprostheses.Journal of neural engineering · 2016Article
- Comparing offline decoding performance in physiologically defined neuronal classes.Journal of neural engineering · 2016Article
- Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2016Article
- Inference and Decoding of Motor Cortex Low-Dimensional Dynamics via Latent State-Space Models.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2016Article
- Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface.Journal of neural engineering · 2016Article
- Brain-controlled muscle stimulation for the restoration of motor function.Neurobiology of disease · 2015Review
- Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms.Journal of neurophysiology · 2014Article
- Real-time estimation and biofeedback of single-neuron firing rates using local field potentials.Nature communications · 2014Article
- Extracting kinetic information from human motor cortical signals.NeuroImage · 2014Article
- How to read neuron-dropping curves?Frontiers in systems neuroscience · 2014Article
- Long term, stable brain machine interface performance using local field potentials and multiunit spikes.Journal of neural engineering · 2013Article
- Neuron Selection by Relative Importance for Neural Decoding of Dexterous Finger Prosthesis Control Application.Biomedical signal processing and control · 2012Article
- Prediction of imagined single-joint movements in a person with high-level tetraplegia.IEEE transactions on bio-medical engineering · 2012Article
- EMG prediction from motor cortical recordings via a nonnegative point-process filter.IEEE transactions on bio-medical engineering · 2012Article
- Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials.Journal of neurophysiology · 2012Article
- Spinal-like regulator facilitates control of a two-degree-of-freedom wrist.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2010Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
A robust identification algorithm has been developed for linear, time-invariant, multiple-input single-output systems, with an emphasis on how this algorithm can be used to estimate the dynamic relationship between a set of neural recordings and related physiological signals. The identification algorithm provides a decomposition of the system output such that each component is uniquely attributable to a specific input signal, and then reduces the complexity of the estimation problem by discarding those input signals that are deemed to be insignificant. Numerical difficulties due to limited input bandwidth and correlations among the inputs are addressed using a robust estimation technique based on singular value decomposition. The algorithm has been evaluated on both simulated and experimental data. The latter involved estimating the relationship between up to 40 simultaneously recorded motor cortical signals and peripheral electromyograms (EMGs) from four upper limb muscles in a freely moving primate. The algorithm performed well in both cases: it provided reliable estimates of the system output and significantly reduced the number of inputs needed for output prediction. For example, although physiological recordings from up to 40 different neuronal signals were available, the input selection algorithm reduced this to 10 neuronal signals that made significant contributions to the recorded EMGs.
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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.