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Conference Paper: Cognitive burden estimation for visuomotor learning with fNIRS

TitleCognitive burden estimation for visuomotor learning with fNIRS
Authors
KeywordsRobotic surgery
neuroergonomics
near infrared spectroscopy
graph theory
cognitive burden
Issue Date2010
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, v. 6363 LNCS, n. PART 3, p. 319-326 How to Cite?
AbstractNovel robotic technologies utilised in surgery need assessment for their effects on the user as well as on technical performance. In this paper, the evolution in 'cognitive burden' across visuomotor learning is quantified using a combination of functional near infrared spectroscopy (fNIRS) and graph theory. The results demonstrate escalating costs within the activated cortical network during the intermediate phase of learning which is manifest as an increase in cognitive burden. This innovative application of graph theory and fNIRS enables the economic evaluation of brain behaviour underpinning task execution and how this may be impacted by novel technology and learning. Consequently, this may shed light on how robotic technologies improve human-machine interaction and augment minimally invasive surgical skills acquisition. This work has significant implications for the development and assessment of emergent robotic technologies at cortical level and in elucidating learning-related plasticity in terms of inter-regional cortical connectivity. © 2010 Springer-Verlag.
Persistent Identifierhttp://hdl.handle.net/10722/200004
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorJames, David Rc C-
dc.contributor.authorOrihuela-Espina, Felipe-
dc.contributor.authorLeff, Daniel Richard-
dc.contributor.authorMylonas, George P.-
dc.contributor.authorKwok, Kawai-
dc.contributor.authorDarzi, Ara W.-
dc.contributor.authorYang, Guangzhong-
dc.date.accessioned2014-07-26T23:11:01Z-
dc.date.available2014-07-26T23:11:01Z-
dc.date.issued2010-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, v. 6363 LNCS, n. PART 3, p. 319-326-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/200004-
dc.description.abstractNovel robotic technologies utilised in surgery need assessment for their effects on the user as well as on technical performance. In this paper, the evolution in 'cognitive burden' across visuomotor learning is quantified using a combination of functional near infrared spectroscopy (fNIRS) and graph theory. The results demonstrate escalating costs within the activated cortical network during the intermediate phase of learning which is manifest as an increase in cognitive burden. This innovative application of graph theory and fNIRS enables the economic evaluation of brain behaviour underpinning task execution and how this may be impacted by novel technology and learning. Consequently, this may shed light on how robotic technologies improve human-machine interaction and augment minimally invasive surgical skills acquisition. This work has significant implications for the development and assessment of emergent robotic technologies at cortical level and in elucidating learning-related plasticity in terms of inter-regional cortical connectivity. © 2010 Springer-Verlag.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.subjectRobotic surgery-
dc.subjectneuroergonomics-
dc.subjectnear infrared spectroscopy-
dc.subjectgraph theory-
dc.subjectcognitive burden-
dc.titleCognitive burden estimation for visuomotor learning with fNIRS-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-15711-0_40-
dc.identifier.scopuseid_2-s2.0-78349233874-
dc.identifier.volume6363 LNCS-
dc.identifier.issuePART 3-
dc.identifier.spage319-
dc.identifier.epage326-
dc.identifier.eissn1611-3349-

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