A co-adaptive brain-computer interface for end users with severe motor impairment.

Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper...

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Autores principales:	Josef Faller, Reinhold Scherer, Ursula Costa, Eloy Opisso, Josep Medina, Gernot R Müller-Putz
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7528af8fa01244b2a404a5e94867d7ab
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spelling	oai:doaj.org-article:7528af8fa01244b2a404a5e94867d7ab2021-11-25T06:08:48ZA co-adaptive brain-computer interface for end users with severe motor impairment.1932-620310.1371/journal.pone.0101168https://doaj.org/article/7528af8fa01244b2a404a5e94867d7ab2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25014055/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper was to evaluate a novel cue-guided, co-adaptive BCI training paradigm with severely impaired volunteers. The co-adaptive BCI supports a non-control state, which is an important step toward intuitive, self-paced control. A secondary aim was to have the same participants operate a specifically designed self-paced BCI training paradigm based on the auto-calibrated classifier. The co-adaptive BCI analyzed the electroencephalogram from three bipolar derivations (C3, Cz, and C4) online, while the 22 end users alternately performed right hand movement imagery (MI), left hand MI and relax with eyes open (non-control state). After less than five minutes, the BCI auto-calibrated and proceeded to provide visual feedback for the MI task that could be classified better against the non-control state. The BCI continued to regularly recalibrate. In every calibration step, the system performed trial-based outlier rejection and trained a linear discriminant analysis classifier based on one auto-selected logarithmic band-power feature. In 24 minutes of training, the co-adaptive BCI worked significantly (p = 0.01) better than chance for 18 of 22 end users. The self-paced BCI training paradigm worked significantly (p = 0.01) better than chance in 11 of 20 end users. The presented co-adaptive BCI complements existing approaches in that it supports a non-control state, requires very little setup time, requires no BCI expert and works online based on only two electrodes. The preliminary results from the self-paced BCI paradigm compare favorably to previous studies and the collected data will allow to further improve self-paced BCI systems for disabled users.Josef FallerReinhold SchererUrsula CostaEloy OpissoJosep MedinaGernot R Müller-PutzPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e101168 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Josef Faller Reinhold Scherer Ursula Costa Eloy Opisso Josep Medina Gernot R Müller-Putz A co-adaptive brain-computer interface for end users with severe motor impairment.
description	Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper was to evaluate a novel cue-guided, co-adaptive BCI training paradigm with severely impaired volunteers. The co-adaptive BCI supports a non-control state, which is an important step toward intuitive, self-paced control. A secondary aim was to have the same participants operate a specifically designed self-paced BCI training paradigm based on the auto-calibrated classifier. The co-adaptive BCI analyzed the electroencephalogram from three bipolar derivations (C3, Cz, and C4) online, while the 22 end users alternately performed right hand movement imagery (MI), left hand MI and relax with eyes open (non-control state). After less than five minutes, the BCI auto-calibrated and proceeded to provide visual feedback for the MI task that could be classified better against the non-control state. The BCI continued to regularly recalibrate. In every calibration step, the system performed trial-based outlier rejection and trained a linear discriminant analysis classifier based on one auto-selected logarithmic band-power feature. In 24 minutes of training, the co-adaptive BCI worked significantly (p = 0.01) better than chance for 18 of 22 end users. The self-paced BCI training paradigm worked significantly (p = 0.01) better than chance in 11 of 20 end users. The presented co-adaptive BCI complements existing approaches in that it supports a non-control state, requires very little setup time, requires no BCI expert and works online based on only two electrodes. The preliminary results from the self-paced BCI paradigm compare favorably to previous studies and the collected data will allow to further improve self-paced BCI systems for disabled users.
format	article
author	Josef Faller Reinhold Scherer Ursula Costa Eloy Opisso Josep Medina Gernot R Müller-Putz
author_facet	Josef Faller Reinhold Scherer Ursula Costa Eloy Opisso Josep Medina Gernot R Müller-Putz
author_sort	Josef Faller
title	A co-adaptive brain-computer interface for end users with severe motor impairment.
title_short	A co-adaptive brain-computer interface for end users with severe motor impairment.
title_full	A co-adaptive brain-computer interface for end users with severe motor impairment.
title_fullStr	A co-adaptive brain-computer interface for end users with severe motor impairment.
title_full_unstemmed	A co-adaptive brain-computer interface for end users with severe motor impairment.
title_sort	co-adaptive brain-computer interface for end users with severe motor impairment.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/7528af8fa01244b2a404a5e94867d7ab
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A co-adaptive brain-computer interface for end users with severe motor impairment.

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