Measuring Workload Through EEG Signals in Simulated Robotic Assisted Surgery Tasks
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1
Purdue University, School of Industrial Engineering, United States
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2
School of Medicine, Indiana University Bloomington, Department of Urology, United States
Robotic assisted surgery (RAS) has been growing in treating cancer and other conditions, but its impact on surgeon workload is unknown. Subjective questionnaires, such as the NASA-TLX, has been the most popular technique to measure workload but is limited due to the subjective nature and disruption of task. In order to measure cognitive load, electroencephalography (EEG) has been used in different domains (e.g., aviation) and this technique can be translated to RAS. Nine participants completed a meditation task and three simulated surgical tasks on a robotic system comprised of the Taurus Dexterous Robot (©SRI International) with a Hydra motion sensing controller (©Razer Inc.; Figure 1). The three simulated tasks were peg drop, peg transfer, and wound debridement. Participants wore an EEG headset which obtained signals from fourteen channels and completed the NASA-TLX questionnaire after each task. The EEG signals were split into theta (4 - 7 Hz), alpha (8 - 12 Hz), and beta (13 - 30 Hz) bandwidths. Power spectrum densities were obtained for analysis. Repeated measures ANOVA with Tukey-Kramer post-hoc adjustment was conducted. Five beta channels – AF3, F7, FC5, P7, and FC6 – showed significant differences between the meditation and peg transfer tasks. There were no significant differences of the mean powers of the alpha and theta bands among all channels and tasks. The mean overall NASA-TLX scores (out of 60) for meditation and peg transfer were 7.5 ± 1.3 and 32.5 ± 16.1, respectively. It was found that EEG signals can be associated with workload measurement in simulated a RAS environment. Future work includes event-related potential segmentations of the task and trend analysis between subjective, perceived workload and EEG signals.
Acknowledgements
This study was funded in part by the Walther Oncology Physical Sciences and Engineering Research Embedding Program.
Keywords:
Workload,
EEG,
Robotic assisted surgery,
human-robot interfaces,
healthcare
Conference:
2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018.
Presentation Type:
Oral Presentation
Topic:
Neuroergonomics
Citation:
Cha
J,
Gonzalez
G,
Sulek
J,
Sundaram
C,
Wachs
J and
Yu
D
(2019). Measuring Workload Through EEG Signals in Simulated Robotic Assisted Surgery Tasks.
Conference Abstract:
2nd International Neuroergonomics Conference.
doi: 10.3389/conf.fnhum.2018.227.00036
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Received:
02 Apr 2018;
Published Online:
27 Sep 2019.
*
Correspondence:
Ms. Jackie Cha, Purdue University, School of Industrial Engineering, West Lafayette, Indiana, 47907, United States, soyoun@purdue.edu