Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees

Agashe, Harshavardhan A.; Paek, Andrew; Contreras-Vidal, José L.

doi:10.1016/bs.pbr.2016.04.016

Cited by 28 publications

(13 citation statements)

References 59 publications

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“…For example, pattern classification has identified a 300 ms window of premotor activity where beta band oscillations convey effector independent representations of grasp vs reach plans (Turella et al, 2016). Further, brain-computer interfaces can decode the kinematics of grasp movements (Agashe et al, 2015(Agashe et al, , 2016Jochumsen et al, 2016;Schwarz et al, 2018) even from single trials (Iturrate et al, 2018). However, to date, no study has mapped the timing of visuomotor transformations of human grasp movements.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Electrophysiological Signals Reveals the Temporal Properties of Visuomotor Computations for Precision Grips

et al. 2019

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The frontoparietal networks underlying grasping movements have been extensively studied, especially using fMRI. Accordingly, whereas much is known about their cortical locus much less is known about the temporal dynamics of visuomotor transformations. Here, we show that multivariate EEG analysis allows for detailed insights into the time course of visual and visuomotor computations of precision grasps. Male and female human participants first previewed one of several objects and, upon its reappearance, reached to grasp it with the thumb and index finger along one of its two symmetry axes. Object shape classifiers reached transient accuracies of 70% at ϳ105 ms, especially based on scalp sites over visual cortex, dropping to lower levels thereafter. Grasp orientation classifiers relied on a system of occipital-to-frontal electrodes. Their accuracy rose concurrently with shape classification but ramped up more gradually, and the slope of the classification curve predicted individual reaction times. Further, cross-temporal generalization revealed that dynamic shape representation involved early and late neural generators that reactivated one another. In contrast, grasp computations involved a chain of generators attaining a sustained state about 100 ms before movement onset. Our results reveal the progression of visual and visuomotor representations over the course of planning and executing grasp movements.

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Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Electrophysiological Signals Reveals the Temporal Properties of Visuomotor Computations for Precision Grips

et al. 2019

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“…There are ongoing developments to enhance myoelectric control through surgical interventions such as targeted muscle reinnervation [36] , [37] and electrode implantation [38] , [39] . Very few studies have demonstrated real time control of a potential upper limb prosthesis with BMIs, such as the control of hand shape with scalp EEG with amputees [40] , and the online control of the grasping and opening of a robotic hand with MEG from paralyzed patients [41] .…”

Section: Overview Of State-of-the-art End Effectorsmentioning

confidence: 99%

A Roadmap Towards Standards for Neurally Controlled End Effectors

Paek

Brantley

Ravindran

et al. 2021

IEEE Open J. Eng. Med. Biol.

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The control and manipulation of various types of end effectors such as powered exoskeletons, prostheses, and 'neural' cursors by brain-machine interface (BMI) systems has been the target of many research projects. A seamless "plug and play" interface between any BMI and end effector is desired, wherein similar user's intent cause similar end effectors to behave identically. This report is based on the outcomes of an IEEE Standards Association Industry Connections working group on End Effectors for Brain-Machine Interfacing that convened to identify and address gaps in the existing standards for BMI-based solutions with a focus on the end-effector component. A roadmap towards standardization of end effectors for BMI systems is discussed by identifying current device standards that are applicable for end effectors. While current standards address basic electrical and mechanical safety, and to some extent, performance requirements, several gaps exist pertaining to unified terminologies, data communication protocols, patient safety and risk mitigation.

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“…Movement-related cortical potentials have also been successfully decoded from EEG-based signals in the rmPFC (Min et al, 2017 ; Koizumi et al, 2018 ). These signals are useful in controlling brain-controlled exoskeletons designed to augment the user's sensorimotor functions (Agashe et al, 2016 ; Hong and Khan, 2017 ; Khan and Hong, 2017 ; Liu et al, 2018 ; Asgher et al, 2021 ). Moreover, a limited number of studies have been conducted to investigate the effect of robot-assisted tasks on cortical reorganization (Youssofzadeh et al, 2016 ; Saita et al, 2017 , 2018 ; Memar and Esfahani, 2018 ; Berger et al, 2019 ; Peters et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Involvement of the Rostromedial Prefrontal Cortex in Human-Robot Interaction: fNIRS Evidence From a Robot-Assisted Motor Task

Watanabe

Ogawa

et al. 2022

Front. Neurorobot.

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Assistive exoskeleton robots are being widely applied in neurorehabilitation to improve upper-limb motor and somatosensory functions. During robot-assisted exercises, the central nervous system appears to highly attend to external information-processing (IP) to efficiently interact with robotic assistance. However, the neural mechanisms underlying this process remain unclear. The rostromedial prefrontal cortex (rmPFC) may be the core of the executive resource allocation that generates biases in the allocation of processing resources toward an external IP according to current behavioral demands. Here, we used functional near-infrared spectroscopy to investigate the cortical activation associated with executive resource allocation during a robot-assisted motor task. During data acquisition, participants performed a right-arm motor task using elbow flexion-extension movements in three different loading conditions: robotic assistive loading (ROB), resistive loading (RES), and non-loading (NON). Participants were asked to strive for kinematic consistency in their movements. A one-way repeated measures analysis of variance and general linear model-based methods were employed to examine task-related activity. We demonstrated that hemodynamic responses in the ventral and dorsal rmPFC were higher during ROB than during NON. Moreover, greater hemodynamic responses in the ventral rmPFC were observed during ROB than during RES. Increased activation in ventral and dorsal rmPFC subregions may be involved in the executive resource allocation that prioritizes external IP during human-robot interactions. In conclusion, these findings provide novel insights regarding the involvement of executive control during a robot-assisted motor task.

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Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees

Cited by 28 publications

References 59 publications

Multivariate Analysis of Electrophysiological Signals Reveals the Temporal Properties of Visuomotor Computations for Precision Grips

Multivariate Analysis of Electrophysiological Signals Reveals the Temporal Properties of Visuomotor Computations for Precision Grips

A Roadmap Towards Standards for Neurally Controlled End Effectors

Involvement of the Rostromedial Prefrontal Cortex in Human-Robot Interaction: fNIRS Evidence From a Robot-Assisted Motor Task

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