Paper Info
Title
Cognitive burden estimation for visuomotor learning with fNIRS
Abstract
Novel 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.
Year
DOI
Venue
2010
10.1007/978-3-642-15711-0_40
MICCAI (3)
Keywords
Field
DocType
near infrared spectroscopy,robotic surgery,skill acquisition,needs assessment,graph theory,neuroergonomics
Graph theory,Computer vision,Computer science,Functional near-infrared spectroscopy,Robotic surgery,Human–computer interaction,Artificial intelligence,Neuroergonomics,Cognition
Conference
Volume
Issue
ISSN
13
Pt 3
0302-9743
ISBN
Citations 
PageRank 
3-642-15710-6
5
0.47
References 
Authors
6
7
Name
Order
Citations
PageRank
David R. C. James1121.63
Felipe Orihuela-Espina210115.78
Daniel Richard Leff370.96
George P. Mylonas421222.01
Ka Wai Kwok516727.10
Ara Darzi649658.41
Guang-Zhong Yang72812297.66