CogniDaVinci: Towards Estimating Mental Workload Modulated by Visual Delays During Telerobotic Surgery - An EEG-based Analysis
Satyam Kumar, Deland H. Liu, Frigyes S. Racz, Manuel Retana, Susheela Sharma, Fumiaki Iwane, Braden P. Murphy, Rory O'Keeffe
Abstract
Communication latency in any delicate telerobotic operation (such as remote surgery over distance) would impose a significant challenge due to the temporal degradation of visual perception and can substantially affect the outcomes. Less is known, however, about the neurophysiological basis of how operators adapt/react to delayed visual feedback. Identification of such neural markers might provide novel ways for future applications to monitor the mental workload (MW). In this study, we recorded electroencephalography (EEG) data from nine users while performing a peg transfer task using the da Vinci Research Kit with three levels of induced visual delay in the video feedback. Our results suggest that spectral EEG-based features can provide markers of the operator's MW modulated by arbitrary visual delay. We also show that the exposure to different visual delays could be successfully classified/detected solely from EEG data, using a Riemannian geometry-based classifier, which highlights the utility of EEG signals for detecting the effect of visual delay on brain activity.
BibTeX
@inproceedings{icra2023_cognidavincitowa,
title = {CogniDaVinci: Towards Estimating Mental Workload Modulated by Visual Delays During Telerobotic Surgery - An EEG-based Analysis},
author = {Satyam Kumar and Deland H. Liu and Frigyes S. Racz and Manuel Retana and Susheela Sharma and Fumiaki Iwane and Braden P. Murphy and Rory O'Keeffe and S. Farokh Atashzar and Farshid Alambeigi and José del R. Millán},
booktitle = {ICRA 2023},
year = {2023}
}