Measuring Mental Workload and Brain Dynamics in Prosthesis Motor Learning over Multi-Session Practice

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dc.contributor.advisorGentili, Rodolphe
dc.contributor.authorAsenso, Maxine
dc.contributor.authorBrown, McCauley
dc.contributor.authorDayanim, Gabriel
dc.contributor.authorDoyle, Erin
dc.contributor.authorGreenbaum, Maya
dc.contributor.authorLavarias, Gabrielle
dc.contributor.authorMercado, Natalia Nava
dc.contributor.authorNguyen, Christina
dc.contributor.authorRussell, Ashley
dc.contributor.authorStill, Alexys
dc.contributor.authorSubramaniam, Carolyn
dc.contributor.authorVarma, Anagha Rama
dc.date.accessioned2022-08-29T18:29:06Z
dc.date.available2022-08-29T18:29:06Z
dc.date.issued2022
dc.descriptionGemstone Team REACHen_US
dc.description.abstractThe capability of humans to adapt their motor behavior and learn new motor skills is critical to interact with their changing environment as well as for integration with new machine interfaces, such as assistive technology (Casadio, Ranganathan, & Mussa-Ivaldia, 2012; Kitago & Krakauer, 2013; Mussa-Ivaldi et al., 2011). Such learning capability depends on the recruitment of cognitivemotor resources (Wickens, 2002). Mental workload (MWL), which is an important component in understanding learning, can be defined as the relationship between the deployment of neural resources and imposed task demands (Sharples & Megaw, 2005; Young et al., 2015). Although a large body of work has examined the behavior and cortical dynamics underlying the motor learning processes, most of this prior effort generally did not examine changes in mental workload through multiple practice sessions and did not consider individuals with upper limb (UL) loss (Marchand, de Graaf, & Jarrassé, 2021; Park & Zahabi, 2022). In this work, UL amputees were approximated by considering healthy individuals using bypass prostheses (Bloomer, Wang, & Kontson, 2018; Wang et al., 2021). Based on the work by Bloomer and Wang, able-bodied individuals can serve as a reasonable proxy for amputees while using these bypass prostheses. From a methodological standpoint, the use of human-body interfaces such as a bypass prosthesis is interesting since it requires participants to acquire a novel and unusual sensorimotor mapping, mitigating the influence of prior motor experiences and ultimately offering a fairly unbiased learning paradigm (Casadio, Ranganathan, & Mussa-Ivaldia, 2012; Mussa-Ivaldi et al., 2011). Thus, we employed this approach here, along with electroencephalography (EEG), which was used to assess the cortical dynamics as participants completed the learning task in order to objectively assess mental workload. In addition, surveys were employed to subjectively assess the level of workload perceived by the participants along with performance (e.g., time, smoothness, number of blocks transported within a fixed time period) collected via an inertial measuring unit. Overall, the aim of this research was to examine the concomitant changes in performance (e.g., number of blocks transported within a fixed time period) and in mental workload (by means of surveys and the cortical dynamics indexed by EEG) that occur when healthy individuals learn to operate a bypass prosthetic device via multi-session practice to perform a variety of motor tasks of daily living. This work can inform not only the human cognitive-motor processes underlying mental workload and performance during learning but also, to some degree, the rehabilitation/training of UL amputees, as well as the design and evaluation of prosthetic devices.en_US
dc.identifierhttps://doi.org/10.13016/k9zy-ox0j
dc.identifier.urihttp://hdl.handle.net/1903/29098
dc.language.isoen_USen_US
dc.relation.isAvailableAtDigital Repository at the University of Maryland
dc.relation.isAvailableAtGemstone Program, University of Maryland (College Park, Md)
dc.subjectGemstone Team REACHen_US
dc.titleMeasuring Mental Workload and Brain Dynamics in Prosthesis Motor Learning over Multi-Session Practiceen_US
dc.typeThesisen_US

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