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        EEG Spectral Feature Modulations Associated with Fatigue in Robot-Mediated Upper Limb Gross and Fine Motor Interactions

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        fnbot_15_788494_2_.pdf (PDF, 5Mb)
        Author
        Dissanayake, Udeshika
        Steuber, Volker
        Amirabdollahian, Farshid
        Attention
        2299/25313
        Abstract
        This paper investigates the EEG spectral feature modulations associated with fatigue induced by robot-mediated upper limb gross and fine motor interactions. Twenty healthy participants were randomly assigned to perform a gross motor interaction with HapticMASTER or a fine motor interaction with SCRIPT passive orthosis for 20 min or until volitional fatigue. Relative and ratio band power measures were estimated from the EEG data recorded before and after the robot-mediated interactions. Paired-samples t-tests found a significant increase in the relative alpha band power and a significant decrease in the relative delta band power due to the fatigue induced by the robot-mediated gross and fine motor interactions. The gross motor task also significantly increased the (θ + α)/β and α/β ratio band power measures, whereas the fine motor task increased the relative theta band power. Furthermore, the robot-mediated gross movements mostly changed the EEG activity around the central and parietal brain regions, whereas the fine movements mostly changed the EEG activity around the frontopolar and central brain regions. The subjective ratings suggest that the gross motor task may have induced physical fatigue, whereas the fine motor task may have induced mental fatigue. Therefore, findings affirm that changes to localised brain activity patterns indicate fatigue developed from the robot-mediated interactions. It can also be concluded that the regional differences in the prominent EEG spectral features are most likely due to the differences in the nature of the task (fine/gross motor and distal/proximal upper limb) that may have differently altered an individual's physical and mental fatigue level. The findings could potentially be used in future to detect and moderate fatigue during robot-mediated post-stroke therapies.
        Publication date
        2022-01-20
        Published in
        Frontiers in Neurorobotics
        Published version
        https://doi.org/10.3389/fnbot.2021.788494
        Other links
        http://hdl.handle.net/2299/25313
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