Grasp detection from human ECoG during natural reach-to-grasp movements.
Various movement parameters of grasping movements, like velocity or type of the grasp, have been successfully decoded from neural activity. However, the question of movement event detection from brain activity, that is, decoding the time at which an event occurred (e.g. movement onset), has been add...
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doaj-e7c81e08ba274ecf8bef2e38a11b746a2020-11-25T01:57:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0181e5465810.1371/journal.pone.0054658Grasp detection from human ECoG during natural reach-to-grasp movements.Tobias PistohlThomas Sebastian Benedikt SchmidtTonio BallAndreas Schulze-BonhageAd AertsenCarsten MehringVarious movement parameters of grasping movements, like velocity or type of the grasp, have been successfully decoded from neural activity. However, the question of movement event detection from brain activity, that is, decoding the time at which an event occurred (e.g. movement onset), has been addressed less often. Yet, this may be a topic of key importance, as a brain-machine interface (BMI) that controls a grasping prosthesis could be realized by detecting the time of grasp, together with an optional decoding of which type of grasp to apply. We, therefore, studied the detection of time of grasps from human ECoG recordings during a sequence of natural and continuous reach-to-grasp movements. Using signals recorded from the motor cortex, a detector based on regularized linear discriminant analysis was able to retrieve the time-point of grasp with high reliability and only few false detections. Best performance was achieved using a combination of signal components from time and frequency domains. Sensitivity, measured by the amount of correct detections, and specificity, represented by the amount of false detections, depended strongly on the imposed restrictions on temporal precision of detection and on the delay between event detection and the time the event occurred. Including neural data from after the event into the decoding analysis, slightly increased accuracy, however, reasonable performance could also be obtained when grasping events were detected 125 ms in advance. In summary, our results provide a good basis for using detection of grasping movements from ECoG to control a grasping prosthesis.http://europepmc.org/articles/PMC3554656?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tobias Pistohl Thomas Sebastian Benedikt Schmidt Tonio Ball Andreas Schulze-Bonhage Ad Aertsen Carsten Mehring |
spellingShingle |
Tobias Pistohl Thomas Sebastian Benedikt Schmidt Tonio Ball Andreas Schulze-Bonhage Ad Aertsen Carsten Mehring Grasp detection from human ECoG during natural reach-to-grasp movements. PLoS ONE |
author_facet |
Tobias Pistohl Thomas Sebastian Benedikt Schmidt Tonio Ball Andreas Schulze-Bonhage Ad Aertsen Carsten Mehring |
author_sort |
Tobias Pistohl |
title |
Grasp detection from human ECoG during natural reach-to-grasp movements. |
title_short |
Grasp detection from human ECoG during natural reach-to-grasp movements. |
title_full |
Grasp detection from human ECoG during natural reach-to-grasp movements. |
title_fullStr |
Grasp detection from human ECoG during natural reach-to-grasp movements. |
title_full_unstemmed |
Grasp detection from human ECoG during natural reach-to-grasp movements. |
title_sort |
grasp detection from human ecog during natural reach-to-grasp movements. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2013-01-01 |
description |
Various movement parameters of grasping movements, like velocity or type of the grasp, have been successfully decoded from neural activity. However, the question of movement event detection from brain activity, that is, decoding the time at which an event occurred (e.g. movement onset), has been addressed less often. Yet, this may be a topic of key importance, as a brain-machine interface (BMI) that controls a grasping prosthesis could be realized by detecting the time of grasp, together with an optional decoding of which type of grasp to apply. We, therefore, studied the detection of time of grasps from human ECoG recordings during a sequence of natural and continuous reach-to-grasp movements. Using signals recorded from the motor cortex, a detector based on regularized linear discriminant analysis was able to retrieve the time-point of grasp with high reliability and only few false detections. Best performance was achieved using a combination of signal components from time and frequency domains. Sensitivity, measured by the amount of correct detections, and specificity, represented by the amount of false detections, depended strongly on the imposed restrictions on temporal precision of detection and on the delay between event detection and the time the event occurred. Including neural data from after the event into the decoding analysis, slightly increased accuracy, however, reasonable performance could also be obtained when grasping events were detected 125 ms in advance. In summary, our results provide a good basis for using detection of grasping movements from ECoG to control a grasping prosthesis. |
url |
http://europepmc.org/articles/PMC3554656?pdf=render |
work_keys_str_mv |
AT tobiaspistohl graspdetectionfromhumanecogduringnaturalreachtograspmovements AT thomassebastianbenediktschmidt graspdetectionfromhumanecogduringnaturalreachtograspmovements AT tonioball graspdetectionfromhumanecogduringnaturalreachtograspmovements AT andreasschulzebonhage graspdetectionfromhumanecogduringnaturalreachtograspmovements AT adaertsen graspdetectionfromhumanecogduringnaturalreachtograspmovements AT carstenmehring graspdetectionfromhumanecogduringnaturalreachtograspmovements |
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