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. © 2010 Springer-Verlag.