A Simple ERP Method for Quantitative Analysis of Cognitive Workload in Myoelectric Prosthesis Control and Human-Machine Interaction

Deeny, Sean P.; Chicoine, Caitlin; Hargrove, Levi J.; Parrish, Todd B.; Jayaraman, Arun

doi:10.1371/journal.pone.0112091

Cited by 51 publications

(39 citation statements)

References 59 publications

(87 reference statements)

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“…In this particular experiment, the higher P300 amplitude obtained on the target tones (compared to the P300 amplitude on the deviant ones) should correspond to lower mental effort of the primary task (walking), meaning that the P300 amplitude is inversely correlated with the easiness to walk-the higher the P300 component on the target tones (and significantly different from the P300 component on the deviant ones), the easier the walking. The oddball paradigm (both auditory or visual) has already been used to assess secondary task performance, for example, assessing distraction while driving 48 , measuring workload for display workers 22 , investigating the ability of pilots to pay attention to the auditory alarms in the cabin during landing 49 and quantify the mental workload of prosthesis control during human-machine interaction 50 .…”

Section: Metabolic Consumption Evaluation a Mobile Spirometry Systemmentioning

confidence: 99%

Sensory feedback restoration in leg amputees improves walking speed, metabolic cost and phantom pain

Petrini

Bumbaširević

Valle

et al. 2019

Nat Med

184

248

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Conventional leg prostheses do not convey sensory information about motion or interaction with the ground to aboveknee amputees, thereby reducing confidence and walking speed in the users that is associated with high mental and physical fatigue 1-4 . The lack of physiological feedback from the remaining extremity to the brain also contributes to the generation of phantom limb pain from the missing leg 5,6 . To determine whether neural sensory feedback restoration addresses these issues, we conducted a study with two transfemoral amputees, implanted with four intraneural stimulation electrodes 7 in the remaining tibial nerve (ClinicalTrials. gov identifier NCT03350061). Participants were evaluated while using a neuroprosthetic device consisting of a prosthetic leg equipped with foot and knee sensors. These sensors drive neural stimulation, which elicits sensations of knee motion and the sole of the foot touching the ground. We found that walking speed and self-reported confidence increased while mental and physical fatigue decreased for both participants during neural sensory feedback compared to the no stimulation trials. Furthermore, participants exhibited reduced phantom limb pain with neural sensory feedback. The results from these proof-of-concept cases provide the rationale for larger population studies investigating the clinical utility of neuroprostheses that restore sensory feedback.Despite advances in the development of lower-limb prosthetics 8 , the potential benefits of restoring sensory feedback from such devices to transfemoral (above-knee) or transtibial (below-knee) amputees has not been investigated. Most surgery techniques 9 and noninvasive methods 10-12 to restore sensory feedback have been tested only in transtibial amputations, which produce a less disabling clinical condition than transfemoral amputation 1,3 . Direct neural stimulation through transversal intrafascicular multichannel electrodes (TIMEs) 7 has enabled upper-limb amputees to feel touch sensations from the missing hand and to exploit them for long-term prosthesis control 13,14 . Only F.M.P. designed the study, developed the software and the overall system integration, performed and supervised the experiments, analyzed the data and wrote and reviewed the paper. M.B. performed the surgeries, was responsible for all the clinical aspects of the study and reviewed the manuscript. G.V. developed the software and the overall system integration, performed the experiments, analyzed the data and reviewed the manuscript. V.I. and S. Mazic collected and analyzed the metabolic measurements. P.M. and B.M. collected and analyzed the EEG measurements. P.C. and T.S. developed the TIME electrodes and delivered technical assistance during the implantation and explanation procedures. F.B. and D.B. developed the software and the overall system integration and performed the experiments. N.K. analyzed the data. D.G. and D.A. designed the hardware and embedded software (real-time control) for STIMEP. K.L. and A.A. participated in the experimental design...

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Section: Metabolic Consumption Evaluation a Mobile Spirometry Systemmentioning

confidence: 99%

Sensory feedback restoration in leg amputees improves walking speed, metabolic cost and phantom pain

Petrini

Bumbaširević

Valle

et al. 2019

Nat Med

184

248

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“…Yet, for prosthetic applications, the current approaches, such as pattern recognition and mode switching require significant training time (Cordella et al, 2016). Moreover, the skill and cognitive load required for continuous prosthetic control increases with the number of available prosthetic DOFs (Deeny et al, 2014). This phenomenon is captured by the dimensionality curse problem in movement planning, which occurs due to the increasing volume of possible solutions with the increasing number of dimensions.…”

Section: Introductionmentioning

confidence: 99%

Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Sobinov

Boots

Gritsenko

et al. 2019

Preprint

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Computational models of the musculoskeletal system are scientific tools used to study human movement, quantify the effects of injury and disease, and plan surgical interventions. Additionally, these models could also be used to intuitively link biological control signals and realistic high-dimensional articulated prosthetic limbs. However, implementing fast and accurate musculoskeletal computations that can be used to control a prosthetic limb in real-time is a challenging problem. As muscles typically span multiple joints, the wrapping over complex geometrical constraints changes their moment arms and length as a function of joint angle and, thus, their ability to generate joint torques. As a result of these biomechanical complexities, calculating these muscle state variables in real-time is a difficult simulation problem. Here, we report a method to accurately and efficiently calculate these variables for the forearm muscles that actuate the hand and wrist across multiple postures. The posture dependent muscle geometry, moment arms and lengths of modeled muscles, were captured using autogenerating polynomials that expanded their optimal selection of terms using information measurements. The iterative process approximated 33 musculotendon actuators, each spanning up to 6 DOFs in an 18 DOF model of the human arm and hand, defined over the full physiological range of motion. Using these polynomials, the entire forearm anatomy could be computed in <10 µs, which is far better than what is required for real-time performance, and with low errors in moment arms (below 5%) and lengths (below 0.4%). Moreover, we demonstrate that the number of elements in these autogenerating polynomials does not increase exponentially with the increase in complexity of muscles, increasing linearly instead. The similar structure and function of muscles are represented with specific invariant polynomial terms. Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of muscles with similar functions, suggesting that the polynomials themselves captured biologically relevant features of muscle structure and function. We propose that this novel method of describing musculoskeletal biomechanics might further improve the applications of detailed and scalable models for the description of human movement.

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“…These systems are designed to promote the performance efficiency of the user and operate through a biocybernetic loop which monitors the users' cognitive state, reacts appropriately and tunes its functioning in an adaptive closed loop (Serbedzija & Fairclough, 2009). Such a biocybernetic control loop could also be incorporated in an active prosthesis in order to monitor and reduce the cognitive workload of the amputee during locomotor tasks (Deeny, Chicoine, Hargrove, Parrish, & Jayaraman, 2014). As for other physiological computing systems, it will allow the amputee and the prosthesis to interact in a collaborative symbiotic manner resulting in a higher motor performance at a lower cognitive workload (Serbedzija & Fairclough, 2009).…”

Section: Introductionmentioning

confidence: 99%

Psychophysiological response to cognitive workload during symmetrical, asymmetrical and dual-task walking

Knaepen

Marušič

Crea

et al. 2015

Human Movement Science

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A Simple ERP Method for Quantitative Analysis of Cognitive Workload in Myoelectric Prosthesis Control and Human-Machine Interaction

Cited by 51 publications

References 59 publications

Sensory feedback restoration in leg amputees improves walking speed, metabolic cost and phantom pain

Sensory feedback restoration in leg amputees improves walking speed, metabolic cost and phantom pain

Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Psychophysiological response to cognitive workload during symmetrical, asymmetrical and dual-task walking

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