Brain–computer interface: Difference between revisions

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Revision as of 21:47, 29 May 2025

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BCIs led to a deeper understanding of neural networks and the [[central nervous system]]. Research has reported that despite neuroscientists' inclination to believe that neurons have the most effect when working together, single neurons can be conditioned through the use of BCIs to fire in a pattern that allows primates to control motor outputs. BCIs led to development of the single neuron insufficiency principle that states that even with a well-tuned firing rate, single neurons can only carry limited information and therefore the highest level of accuracy is achieved by recording ensemble firings. Other principles discovered with BCIs include the neuronal multitasking principle, the neuronal mass principle, the neural degeneracy principle, and the plasticity principle.<ref>{{cite journal | vauthors = Nicolelis MA, Lebedev MA | title = Principles of neural ensemble physiology underlying the operation of brain-machine interfaces | journal = Nature Reviews. Neuroscience | volume = 10 | issue = 7 | pages = 530–540 | date = July 2009 | pmid = 19543222 | doi = 10.1038/nrn2653 | s2cid = 9290258 }}</ref>

BCIs are proposed to be applied by users without disabilities. Passive BCIs allow for assessing and interpreting changes in the user state during Human-Computer Interaction ([[Human-ComputerHuman–computer Interaction|HCIinteraction]] (HCI). In a secondary, implicit control loop, the system adapts to its user, improving its [[usability]].<ref name=":0">{{cite journal |vauthors=Zander TO, Kothe C |date=April 2011 |title=Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general |journal=Journal of Neural Engineering |volume=8 |issue=2 |pages=025005 |bibcode=2011JNEng...8b5005Z |doi=10.1088/1741-2560/8/2/025005 |pmid=21436512 |s2cid=37168897}}</ref>

BCI systems can potentially be used to encode signals from the periphery. These sensory BCI devices enable real-time, behaviorally-relevant decisions based upon closed-loop neural stimulation.<ref>{{cite journal | vauthors = Richardson AG, Ghenbot Y, Liu X, Hao H, Rinehart C, DeLuccia S, Torres Maldonado S, Boyek G, Zhang M, Aflatouni F, Van der Spiegel J, Lucas TH | display-authors = 6 | title = Learning active sensing strategies using a sensory brain-machine interface | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 116 | issue = 35 | pages = 17509–17514 | date = August 2019 | pmid = 31409713 | pmc = 6717311 | doi = 10.1073/pnas.1909953116 | bibcode = 2019PNAS..11617509R | doi-access = free }}</ref>