Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.

In this study, we used electrocorticographic (ECoG) signals to extract the onset of arm movement as well as the velocity of the hand as a function of time. ECoG recordings were obtained from three individuals while they performed reaching tasks in the left, right and forward directions. The ECoG ele...

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Main Authors: Omid Talakoub, Cesar Marquez-Chin, Milos R Popovic, Jessie Navarro, Erich T Fonoff, Clement Hamani, Willy Wong
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5606933?pdf=render
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author Omid Talakoub
Cesar Marquez-Chin
Milos R Popovic
Jessie Navarro
Erich T Fonoff
Clement Hamani
Willy Wong
author_facet Omid Talakoub
Cesar Marquez-Chin
Milos R Popovic
Jessie Navarro
Erich T Fonoff
Clement Hamani
Willy Wong
author_sort Omid Talakoub
collection DOAJ
description In this study, we used electrocorticographic (ECoG) signals to extract the onset of arm movement as well as the velocity of the hand as a function of time. ECoG recordings were obtained from three individuals while they performed reaching tasks in the left, right and forward directions. The ECoG electrodes were placed over the motor cortex contralateral to the moving arm. Movement onset was detected from gamma activity with near perfect accuracy (> 98%), and a multiple linear regression model was used to predict the trajectory of the reaching task in three-dimensional space with an accuracy exceeding 85%. An adaptive selection of frequency bands was used for movement classification and prediction. This demonstrates the efficacy of developing a real-time brain-machine interface for arm movements with as few as eight ECoG electrodes.
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spelling doaj.art-7924d2e9cf454c43bbaf5eb7b5b2cf2e2022-12-21T22:21:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01129e018254210.1371/journal.pone.0182542Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.Omid TalakoubCesar Marquez-ChinMilos R PopovicJessie NavarroErich T FonoffClement HamaniWilly WongIn this study, we used electrocorticographic (ECoG) signals to extract the onset of arm movement as well as the velocity of the hand as a function of time. ECoG recordings were obtained from three individuals while they performed reaching tasks in the left, right and forward directions. The ECoG electrodes were placed over the motor cortex contralateral to the moving arm. Movement onset was detected from gamma activity with near perfect accuracy (> 98%), and a multiple linear regression model was used to predict the trajectory of the reaching task in three-dimensional space with an accuracy exceeding 85%. An adaptive selection of frequency bands was used for movement classification and prediction. This demonstrates the efficacy of developing a real-time brain-machine interface for arm movements with as few as eight ECoG electrodes.http://europepmc.org/articles/PMC5606933?pdf=render
spellingShingle Omid Talakoub
Cesar Marquez-Chin
Milos R Popovic
Jessie Navarro
Erich T Fonoff
Clement Hamani
Willy Wong
Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.
PLoS ONE
title Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.
title_full Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.
title_fullStr Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.
title_full_unstemmed Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.
title_short Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.
title_sort reconstruction of reaching movement trajectories using electrocorticographic signals in humans
url http://europepmc.org/articles/PMC5606933?pdf=render
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