ReviewFrontiers in systems neuroscience2014
Decoding methods for neural prostheses: where have we reached?
Review in Frontiers in systems neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
- Microelectrode arrays cultured with in vitro neural networks for motion control tasks: encoding and decoding progress and advances.Microsystems & nanoengineering · 2025Review
- Translational opportunities and challenges of invasive electrodes for neural interfaces.Nature biomedical engineering · 2023Review
- In-silico development and assessment of a Kalman filter motor decoder for prosthetic hand control.Computers in biology and medicine · 2021Article
- Decoding mood.Nature biotechnology · 2018Article
- Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review.Frontiers in neuroscience · 2018Review
- Interfacing to the brain's motor decisions.Journal of neurophysiology · 2017Review
- Representation of memories in the cortical-hippocampal system: Results from the application of population similarity analyses.Neurobiology of learning and memory · 2016Review
- Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering.PLoS computational biology · 2016Article
- An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces.Frontiers in neuroscience · 2016Article
Corrections and comments
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Authors and funding
1 author.
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Abstract
This article reviews advances in decoding methods for brain-machine interfaces (BMIs). Recent work has focused on practical considerations for future clinical deployment of prosthetics. This review is organized by open questions in the field such as what variables to decode, how to design neural tuning models, which neurons to select, how to design models of desired actions, how to learn decoder parameters during prosthetic operation, and how to adapt to changes in neural signals and neural tuning. The concluding discussion highlights the need to design and test decoders within the context of their expected use and the need to answer the question of how much control accuracy is good enough for a prosthetic.
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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.