Probability-Based Prediction of Activity in Multiple Arm Muscles: Implications for Functional Electrical Stimulation

Anderson, Chad V.; Fuglevand, Andrew J.

doi:10.1152/jn.00956.2007

Cited by 14 publications

(33 citation statements)

References 43 publications

(35 reference statements)

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“…But the majority of movements we recorded cannot be predicted based on EMG activation patterns alone (>70%) and the same movement can be evoked by activation of different muscles. More extensive surface EMG recordings may not be able to access all of the information necessary to predict movements (Anderson and Fuglevand 2008) and such data may be highly variable by individual (Staudenmann et al 2008). …”

Section: Discussionmentioning

confidence: 99%

Arm movement maps evoked by cortical magnetic stimulation in a robotic environment

2010

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Many neurological diseases result in a severe inability to reach for which there is no proven therapy. Promising new interventions to address reaching rehabilitation using robotic training devices are currently under investigation in clinical trials but the neural mechanisms that underlie these interventions are not understood. Transcranial magnetic stimulation (TMS) may be used to probe such mechanisms quickly and non-invasively, by mapping muscle and movement representations in the primary motor cortex (M1). Here we investigate movement maps in healthy young subjects at rest using TMS in the robotic environment, with the goal of determining the range of TMS accessible movements, as a starting point for the study of cortical plasticity in combination with robotic therapy. We systematically stimulated the left motor cortex of 14 normal volunteers while the right hand and forearm rested in the cradle of a two degree-of-freedom planar rehabilitation robot (IMT). Maps were created by applying 10 stimuli at each of 9 locations (3 × 3 cm grid) centered on the M1 movement hotspot for each subject, defined as the stimulation location that elicited robot cradle movements of the greatest distance. TMS-evoked movement kinematics were measured by the robotic encoders and ranged in magnitude from 0-3 cm. Movement maps varied by subject and by location within a subject. However, movements were very consistent within a single stimulation location for a given subject. Movement vectors remained relatively constant (limited to <90 degree section of the planar field) within some subjects across the entire map, while others covered a wider range of directions. This may be due to individual differences in cortical physiology or anatomy, resulting in a practical limit to the areas that are TMS-accessible. This study provides a baseline inventory of possible TMSevoked arm movements in the robotic reaching trainer, and thus may provide a real-time, noninvasive platform for neurophysiology based evaluation and therapy in motor rehabilitation settings.Corresponding Author Contact Details: George F. Wittenberg, MD, PhD, Baltimore VAMC, GRECC (BT/18/GR), 10 N Greene Street, Baltimore, MD 21201-1524, Phone: (410) Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. (Georgopoulos et al. 1992), but the study of multijoint movement end points has not, to our knowledge, been undertaken. Such simple end point measurements are currently used as markers for functional improvement by therapists (e.g. reaching towards a cup on a table top, collecting change from a counter top) and in upper extremity en...

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Section: Discussionmentioning

confidence: 99%

Arm movement maps evoked by cortical magnetic stimulation in a robotic environment

2010

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“…To a reasonable degree, the generalized transfer function accomplished this aim for different subjects, different muscles, and for different mechanical situations. Therefore, it would seem that this approach holds promise for use with an algorithm that predicts the EMG activity across multiple muscles associated with desired motor behaviours (Seifert and Fuglevand, 2002; Blana et al, 2008; Anderson and Fuglevand, 2008; Johnson and Fuglevand, 2009; Pohlmeyer et al, 2009). Furthermore, if a patient were equipped with an appropriate interface to the CNS, it seems feasible that kinematic and kinetic features of desired motor behaviours could be identified directly from the cerebral cortex (Wessberg et al, 2000; Serruya et al, 2002; Schalk et al, 2008; Velliste et al, 2008; Pohlmeyer et al, 2009; Hatsopoulos and Donoghue, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, we envision a three part system in which (1) recordings of activity from the cerebral cortex provide a moment-by-moment representation of the desired trajectory of the limb (e.g. Georgopoulus et al 1989; Schwartz 1993), (2) the complex patterns of muscle activity associated with the desired limb trajectory are predicted moment-by-moment using probabilistic algorithms (Seifert and Fuglevand 2002, Anderson and Fuglevand 2008, Johnson and Fuglevand 2009), and (3) the predicted patterns of muscle activity are converted to patterns of electrical stimulation that elicit the intended movement. Related to the third part of this system, here we have shown that it is possible, using a generalized transfer function, to accurately reproduce movements (or isometric torque profiles) using electrical stimulation patterns derived from muscle activity patterns.…”

Section: Discussionmentioning

confidence: 99%

“…Using probabilistic approaches (Seifert and Fuglevand, 2002; Anderson and Fuglevand, 2008; Johnson and Fuglevand, 2009), we have shown that it is possible to identify complex patterns of muscle activity associated with natural movements of the upper limb. The number of different behaviours that can be identified with this method is theoretically unlimited.…”

Section: Introductionmentioning

confidence: 99%

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Mimicking muscle activity with electrical stimulation

Johnson

Fuglevand

2011

J. Neural Eng.

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Functional electrical stimulation (FES) is a rehabilitation technology that can restore some degree of motor function in individuals that have sustained a spinal cord injury or stroke. One way to identify the spatio-temporal patterns of muscle stimulation needed to elicit complex upper limb movements is to use electromyographic (EMG) activity recorded from able-bodied subjects as a template for electrical stimulation. However, this requires a transfer function to convert the recorded (or predicted) EMG signals into an appropriate pattern of electrical stimulation. Here we develop a generalized transfer function that maps EMG activity into a stimulation pattern that modulates muscle output by varying both the pulse frequency and the pulse amplitude. We show that the stimulation patterns produced by this transfer function mimic the active state measured by EMG insofar as they reproduce with good fidelity complex patterns of joint torque and joint displacement.

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“…Other investigators have reported such methods also (Wessberg et al, 2000; Anderson and Fuglevand, 2008; Fried et al, 2011). In our work, subjects made spontaneous movements and by analyzing the EEG, we could predict movement before its occurrence.…”

Section: Introductionmentioning

confidence: 99%

What We Think before a Voluntary Movement

Schneider

Houdayer

Bai

et al. 2013

Journal of Cognitive Neuroscience

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A central feature of voluntary movement is the sense of volition, but when this sense arises in the course of movement formulation and execution is not clear. Many studies have explored how the brain might be actively preparing movement prior to the sense of volition, however, because the timing of the sense of volition has depended on subjective and retrospective judgements these findings are still regarded with a degree of scepticism. Electroencephalographic (EEG) events such as beta event-related desynchronization (βERD) and movement-related cortical potentials (MRCPs) are associated with the brain’s programming of movement. Using an optimized EEG signal derived from multiple variables we were able to make real-time predictions of movements in advance of their occurrence with a low false positive rate. We asked subjects what they were thinking at the time of prediction: sometimes they were thinking about movement, and other times they were not. Our results indicate that the brain can be preparing to make voluntary movements while subjects are thinking about something else.

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Probability-Based Prediction of Activity in Multiple Arm Muscles: Implications for Functional Electrical Stimulation

Cited by 14 publications

References 43 publications

Arm movement maps evoked by cortical magnetic stimulation in a robotic environment

Arm movement maps evoked by cortical magnetic stimulation in a robotic environment

Mimicking muscle activity with electrical stimulation

What We Think before a Voluntary Movement

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