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Brain–machine interface for eye movements

Graf, Arnulf B. A. and Andersen, Richard A. (2014) Brain–machine interface for eye movements. Proceedings of the National Academy of Sciences of the United States of America, 111 (49). pp. 17630-17635. ISSN 0027-8424. PMCID PMC4267382. http://resolver.caltech.edu/CaltechAUTHORS:20141127-084706620

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Abstract

A number of studies in tetraplegic humans and healthy nonhuman primates (NHPs) have shown that neuronal activity from reach-related cortical areas can be used to predict reach intentions using brain–machine interfaces (BMIs) and therefore assist tetraplegic patients by controlling external devices (e.g., robotic limbs and computer cursors). However, to our knowledge, there have been no studies that have applied BMIs to eye movement areas to decode intended eye movements. In this study, we recorded the activity from populations of neurons from the lateral intraparietal area (LIP), a cortical node in the NHP saccade system. Eye movement plans were predicted in real time using Bayesian inference from small ensembles of LIP neurons without the animal making an eye movement. Learning, defined as an increase in the prediction accuracy, occurred at the level of neuronal ensembles, particularly for difficult predictions. Population learning had two components: an update of the parameters of the BMI based on its history and a change in the responses of individual neurons. These results provide strong evidence that the responses of neuronal ensembles can be shaped with respect to a cost function, here the prediction accuracy of the BMI. Furthermore, eye movement plans could be decoded without the animals emitting any actual eye movements and could be used to control the position of a cursor on a computer screen. These findings show that BMIs for eye movements are promising aids for assisting paralyzed patients.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1073/pnas.1419977111 DOIArticle
http://www.pnas.org/content/111/49/17630.abstractPublisherArticle
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267382/PubMed CentralArticle
Additional Information:Copyright © 2014 National Academy of Sciences. Contributed by Richard A. Andersen, October 24, 2014 (sent for review July 29, 2014; reviewed by Apostolos P. Georgopoulos and Ranulfo Romo). We thank M. Yanike for helpful comments on the manuscript, K. Pejsa for animal care, and V. Shcherbatyuk and T. Yao for technical and administrative assistance. This research was supported by National Institutes of Health Research Grants EY005522, EY013337, and EY007492 and the Boswell Foundation. Author contributions: A.B.A.G. and R.A.A. designed research; A.B.A.G. performed research; A.B.A.G. analyzed data; and A.B.A.G. and R.A.A. wrote the paper. The authors declare no conflict of interest.
Funders:
Funding AgencyGrant Number
NIHEY005522
NIHEY013337
NIHEY007492
Boswell FoundationUNSPECIFIED
Subject Keywords:learning; lateral intraparietal area; brain–machine interface; eye movements; saccades
PubMed Central ID:PMC4267382
Record Number:CaltechAUTHORS:20141127-084706620
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20141127-084706620
Official Citation:Arnulf B. A. Graf and Richard A. Andersen Brain–machine interface for eye movements PNAS 2014 111 (49) 17630-17635; published ahead of print November 24, 2014, doi:10.1073/pnas.1419977111
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:52189
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:01 Dec 2014 17:01
Last Modified:10 Mar 2016 00:48

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