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An Electrocorticographic Brain Interface in an Individual with Tetraplegia

Overview of attention for article published in PLOS ONE, February 2013
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Title
An Electrocorticographic Brain Interface in an Individual with Tetraplegia
Published in
PLOS ONE, February 2013
DOI 10.1371/journal.pone.0055344
Pubmed ID
Authors

Wei Wang, Jennifer L. Collinger, Alan D. Degenhart, Elizabeth C. Tyler-Kabara, Andrew B. Schwartz, Daniel W. Moran, Douglas J. Weber, Brian Wodlinger, Ramana K. Vinjamuri, Robin C. Ashmore, John W. Kelly, Michael L. Boninger

Abstract

Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 402 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 3%
United Kingdom 3 <1%
Germany 2 <1%
Italy 2 <1%
Brazil 1 <1%
Malaysia 1 <1%
Mexico 1 <1%
Hungary 1 <1%
Unknown 379 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 109 27%
Researcher 66 16%
Student > Bachelor 48 12%
Student > Master 44 11%
Professor 19 5%
Other 60 15%
Unknown 56 14%
Readers by discipline Count As %
Engineering 122 30%
Agricultural and Biological Sciences 50 12%
Neuroscience 49 12%
Medicine and Dentistry 34 8%
Computer Science 28 7%
Other 50 12%
Unknown 69 17%