ReviewJournal of neurophysiology2017
Physiological properties of brain-machine interface input signals.
Review in Journal of neurophysiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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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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.
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Who cites it
19 citing papers in PubMed.
- Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.Nature biomedical engineering · 2026Article
- A general model based on Riemannian manifold for stable decoding movement trajectory from ECoG signals.iScience · 2026Article
- Beta burst characteristics predict instantaneous hand movements in Parkinson's disease.Neurobiology of disease · 2025Article
- Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling.Advances in neural information processing systems · 2025Article
- Enhancing neuroprosthesis calibration: the advantage of integrating prior training over exclusive use of new data.Journal of neural engineering · 2024Article
- Machine learning-based high-frequency neuronal spike reconstruction from low-frequency and low-sampling-rate recordings.Nature communications · 2024Article
- A Review of Brain Activity and EEG-Based Brain-Computer Interfaces for Rehabilitation Application.Bioengineering (Basel, Switzerland) · 2022Review
- Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain-machine interface for brain injury.Journal of neural engineering · 2022Article
- The science and engineering behind sensitized brain-controlled bionic hands.Physiological reviews · 2022Review
- Potential therapeutic mechanism of deep brain stimulation of the nucleus accumbens in obsessive-compulsive disorder.Frontiers in cellular neuroscience · 2022Article
- Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation.Seminars in neurology · 2021Article
- The combination of brain-computer interfaces and artificial intelligence: applications and challenges.Annals of translational medicine · 2020Review
- Electrocorticogram (ECoG) Is Highly Informative in Primate Visual Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- 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
- Speech synthesis from ECoG using densely connected 3D convolutional neural networks.Journal of neural engineering · 2019Article
- Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond.Nature reviews. Neurology · 2019Article
- Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2019Review
- A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence.Frontiers in neuroscience · 2018Review
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2 authors.
Funding
Abstract
Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance-including movement-related information, longevity, and stability-of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability.
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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.