Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.

During 0.1-0.2% of operations with general anesthesia, patients become aware during surgery. Unfortunately, pharmacologically paralyzed patients cannot seek attention by moving. Their attempted movements may however induce detectable EEG changes over the motor cortex. Here, methods from the area of...

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Autores principales:	Yvonne M Blokland, Jason D R Farquhar, Jo Mourisse, Gert J Scheffer, Jos G C Lerou, Jörgen Bruhn
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c87bd8aff22a4ace9d7746009f9d4404
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id	oai:doaj.org-article:c87bd8aff22a4ace9d7746009f9d4404
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spelling	oai:doaj.org-article:c87bd8aff22a4ace9d7746009f9d44042021-11-18T07:06:24ZTowards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.1932-620310.1371/journal.pone.0044336https://doaj.org/article/c87bd8aff22a4ace9d7746009f9d44042012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22970202/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203During 0.1-0.2% of operations with general anesthesia, patients become aware during surgery. Unfortunately, pharmacologically paralyzed patients cannot seek attention by moving. Their attempted movements may however induce detectable EEG changes over the motor cortex. Here, methods from the area of movement-based brain-computer interfacing are proposed as a novel direction in anesthesia monitoring. Optimal settings for development of such a paradigm are studied to allow for a clinically feasible system. A classifier was trained on recorded EEG data of ten healthy non-anesthetized participants executing 3-second movement tasks. Extensive analysis was performed on this data to obtain an optimal EEG channel set and optimal features for use in a movement detection paradigm. EEG during movement could be distinguished from EEG during non-movement with very high accuracy. After a short calibration session, an average classification rate of 92% was obtained using nine EEG channels over the motor cortex, combined movement and post-movement signals, a frequency resolution of 4 Hz and a frequency range of 8-24 Hz. Using Monte Carlo simulation and a simple decision making paradigm, this translated into a probability of 99% of true positive movement detection within the first two and a half minutes after movement onset. A very low mean false positive rate of <0.01% was obtained. The current results corroborate the feasibility of detecting movement-related EEG signals, bearing in mind the clinical demands for use during surgery. Based on these results further clinical testing can be initiated.Yvonne M BloklandJason D R FarquharJo MourisseGert J SchefferJos G C LerouJörgen BruhnPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 9, p e44336 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yvonne M Blokland Jason D R Farquhar Jo Mourisse Gert J Scheffer Jos G C Lerou Jörgen Bruhn Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.
description	During 0.1-0.2% of operations with general anesthesia, patients become aware during surgery. Unfortunately, pharmacologically paralyzed patients cannot seek attention by moving. Their attempted movements may however induce detectable EEG changes over the motor cortex. Here, methods from the area of movement-based brain-computer interfacing are proposed as a novel direction in anesthesia monitoring. Optimal settings for development of such a paradigm are studied to allow for a clinically feasible system. A classifier was trained on recorded EEG data of ten healthy non-anesthetized participants executing 3-second movement tasks. Extensive analysis was performed on this data to obtain an optimal EEG channel set and optimal features for use in a movement detection paradigm. EEG during movement could be distinguished from EEG during non-movement with very high accuracy. After a short calibration session, an average classification rate of 92% was obtained using nine EEG channels over the motor cortex, combined movement and post-movement signals, a frequency resolution of 4 Hz and a frequency range of 8-24 Hz. Using Monte Carlo simulation and a simple decision making paradigm, this translated into a probability of 99% of true positive movement detection within the first two and a half minutes after movement onset. A very low mean false positive rate of <0.01% was obtained. The current results corroborate the feasibility of detecting movement-related EEG signals, bearing in mind the clinical demands for use during surgery. Based on these results further clinical testing can be initiated.
format	article
author	Yvonne M Blokland Jason D R Farquhar Jo Mourisse Gert J Scheffer Jos G C Lerou Jörgen Bruhn
author_facet	Yvonne M Blokland Jason D R Farquhar Jo Mourisse Gert J Scheffer Jos G C Lerou Jörgen Bruhn
author_sort	Yvonne M Blokland
title	Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.
title_short	Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.
title_full	Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.
title_fullStr	Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.
title_full_unstemmed	Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.
title_sort	towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/c87bd8aff22a4ace9d7746009f9d4404
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Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.

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