On the applicability of brain reading for predictive human-machine interfaces in robotics.

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant b...

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Autores principales:	Elsa Andrea Kirchner, Su Kyoung Kim, Sirko Straube, Anett Seeland, Hendrik Wöhrle, Mario Michael Krell, Marc Tabie, Manfred Fahle
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/394f7c720b0b4c4381bd7ab8b6baeeef
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id	oai:doaj.org-article:394f7c720b0b4c4381bd7ab8b6baeeef
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spelling	oai:doaj.org-article:394f7c720b0b4c4381bd7ab8b6baeeef2021-11-18T08:41:53ZOn the applicability of brain reading for predictive human-machine interfaces in robotics.1932-620310.1371/journal.pone.0081732https://doaj.org/article/394f7c720b0b4c4381bd7ab8b6baeeef2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24358125/?tool=EBIhttps://doaj.org/toc/1932-6203The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.Elsa Andrea KirchnerSu Kyoung KimSirko StraubeAnett SeelandHendrik WöhrleMario Michael KrellMarc TabieManfred FahlePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e81732 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Elsa Andrea Kirchner Su Kyoung Kim Sirko Straube Anett Seeland Hendrik Wöhrle Mario Michael Krell Marc Tabie Manfred Fahle On the applicability of brain reading for predictive human-machine interfaces in robotics.
description	The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.
format	article
author	Elsa Andrea Kirchner Su Kyoung Kim Sirko Straube Anett Seeland Hendrik Wöhrle Mario Michael Krell Marc Tabie Manfred Fahle
author_facet	Elsa Andrea Kirchner Su Kyoung Kim Sirko Straube Anett Seeland Hendrik Wöhrle Mario Michael Krell Marc Tabie Manfred Fahle
author_sort	Elsa Andrea Kirchner
title	On the applicability of brain reading for predictive human-machine interfaces in robotics.
title_short	On the applicability of brain reading for predictive human-machine interfaces in robotics.
title_full	On the applicability of brain reading for predictive human-machine interfaces in robotics.
title_fullStr	On the applicability of brain reading for predictive human-machine interfaces in robotics.
title_full_unstemmed	On the applicability of brain reading for predictive human-machine interfaces in robotics.
title_sort	on the applicability of brain reading for predictive human-machine interfaces in robotics.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/394f7c720b0b4c4381bd7ab8b6baeeef
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On the applicability of brain reading for predictive human-machine interfaces in robotics.

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