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Adaptation of Rehabilitation System Based on User’s Mental Engagement

[+] Author Affiliations
Ehsan T. Esfahani, Shrey Pareek, Pramod Chembrammel, Mostafa Ghobadi

University at Buffalo - SUNY, Buffalo, NY

Thenkurussi Kesavadas

University of Illinois, UIUC, Champaign, IL

Paper No. DETC2015-47720, pp. V003T14A017; 7 pages
doi:10.1115/DETC2015-47720
From:
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 17th International Conference on Advanced Vehicle Technologies; 12th International Conference on Design Education; 8th Frontiers in Biomedical Devices
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5710-6
  • Copyright © 2015 by ASME

abstract

Recognition of user’s mental engagement is imperative to the success of robotic rehabilitation. The paper explores the novel paradigm in robotic rehabilitation of using Passive BCI as opposed to the conventional Active ones. We have designed experiments to determine a user’s level of mental engagement. In our experimental study, we record the brain activity of 3 healthy subjects during multiple sessions where subjects need to navigate through a maze using a haptic system with variable resistance/assistance. Using the data obtained through the experiments we highlight the drawbacks of using conventional workload metrics as indicators of human engagement, thus asserting that Motor and Cognitive Workloads be differentiated.

Additionally we propose a new set of features: differential PSD of Cz-Poz at alpha, Beta and Sigma band, (Mental engagement) and relative C3-C4 at beta (Motor Workload) to distinguish Normal Cases from those instances when haptic where applied with an accuracy of 92.93%. Mental engagement is calculated using the power spectral density of the Theta band (4–7 Hz) in the parietal-midline (Pz) with respect to the central midline (Cz). The above information can be used to adjust robotic rehabilitation parameters I accordance with the user’s needs. The adjustment may be in the force levels, difficulty level of the task or increasing the speed of the task.

Copyright © 2015 by ASME

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