ArticleJournal of computational neuroscience2015
Recasting brain-machine interface design from a physical control system perspective.
Article in Journal of computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Less is more: selection from a small set of options improves BCI velocity control.Journal of neural engineering · 2025Article
- Less is more: selection from a small set of options improves BCI velocity control.bioRxiv : the preprint server for biology · 2024Article
- Neural Plasticity in Sensorimotor Brain-Machine Interfaces.Annual review of biomedical engineering · 2023Review
- Distributed processing of movement signaling.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements.Scientific reports · 2018Article
- The critical stability task: quantifying sensory-motor control during ongoing movement in nonhuman primates.Journal of neurophysiology · 2018Article
- Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future.Brain computer interfaces (Abingdon, England) · 2017Article
- Neuroprosthetic Decoder Training as Imitation Learning.PLoS computational biology · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the goal of improving the quality of life for people suffering from various motor control disorders, brain-machine interfaces provide direct neural control of prosthetic devices by translating neural signals into control signals. These systems act by reading motor intent signals directly from the brain and using them to control, for example, the movement of a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into the movement of the cursor? Most approaches have focused on this problem from an estimation standpoint, i.e., decoders are designed to return the best estimate of motor intent possible, under various sets of assumptions about how the recorded neural signals represent motor intent. Here we recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. This framework leads to new interpretations of why certain types of decoders have been shown to perform better than others. These results have implications for understanding how motor neurons are recruited to perform various tasks, and may lend insight into the brain's ability to conceptualize artificial systems.
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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.