Online feedback control of functional electrical stimulation using dorsal root ganglia recordings

Bauman, Matthew J.; Bruns, Tim M.; Wagenaar, Joost B.; Gaunt, Robert A.; Weber, Douglas J.

doi:10.1109/iembs.2011.6091831

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Most artifacts were automatically rejected by the clustering algorithm. In addition, a simple artifact rejection algorithm was implemented in the RZ2 processor, as described in [34]. Briefly, if the sum of spike-threshold crossing events across all channels during a 400 µs detection window exceeded a set limit (54 channels; 60%) then all events in a corresponding 2 ms rejection window were excluded from the spike count vector.…”

Section: Methodsmentioning

confidence: 99%

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Real-time control of hind limb functional electrical stimulation using feedback from dorsal root ganglia recordings

Bruns¹,

Wagenaar²,

Bauman³

et al. 2013

J. Neural Eng.

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Objective Functional electrical stimulation (FES) approaches often utilize an open-loop controller to drive state transitions. The addition of sensory feedback may allow for closed-loop control that can respond effectively to perturbations and muscle fatigue. Approach We evaluated the use of natural sensory nerve signals obtained with penetrating microelectrode arrays in lumbar dorsal root ganglia (DRG) as real-time feedback for closed-loop control of FES-generated hind limb stepping in anesthetized cats. Main results Leg position feedback was obtained in near real-time at 50 ms intervals by decoding the firing rates of more than 120 DRG neurons recorded simultaneously. Over 5 m of effective linear distance was traversed during closed-loop stepping trials in each of two cats. The controller compensated effectively for perturbations in the stepping path when DRG sensory feedback was provided. The presence of stimulation artifacts and the quality of DRG unit sorting did not significantly affect the accuracy of leg position feedback obtained from the linear decoding model as long as at least 20 DRG units were included in the model. Significance This work demonstrates the feasibility and utility of closed-loop FES control based on natural neural sensors. Further work is needed to improve the controller and electrode technologies and to evaluate long-term viability.

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Section: Methodsmentioning

confidence: 99%

“…Our results indicate that while stimulation artifacts can contaminate signals recorded from DRG neurons, there was not a significant impact upon limb position estimates. Preliminary results have been presented in poster form at conferences [34, 35]. …”

Section: Introductionmentioning

confidence: 99%

Real-time control of hind limb functional electrical stimulation using feedback from dorsal root ganglia recordings

Bruns¹,

Wagenaar²,

Bauman³

et al. 2013

J. Neural Eng.

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“…Finally, the current was maintained in 0.5 mA and the frequency was lowered to 3 Hz (experiment #4), which gave rise to a positive effect on axonal growth, showing an increased axonal extension and axonal sprouting for the electrically stimulated DRG, compared with the non-stimulated ones. Several studies of neurostimulation of DRG have observed that low frequencies (<20 Hz) have a beneficial effect on pain relief, inducing the dorsal horn of the spinal cord inhibition via the activation of low threshold mechanoreceptors and the release of endorphins and dynorphins [ 76 , 77 , 78 , 79 ]. Therefore, we could say that the applied stimulation frequency of 3 Hz is within the physiological range of DRG activation.…”

Section: Resultsmentioning

confidence: 99%

Electrical Stimulation Increases Axonal Growth from Dorsal Root Ganglia Co-Cultured with Schwann Cells in Highly Aligned PLA-PPy-Au Microfiber Substrates

Roca

Requena

Pradas

et al. 2022

IJMS

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Nerve regeneration is a slow process that needs to be guided for distances greater than 5 mm. For this reason, different strategies are being studied to guide axonal growth and accelerate the axonal growth rate. In this study, we employ an electroconductive fibrillar substrate that is able to topographically guide axonal growth while accelerating the axonal growth rate when subjected to an exogenous electric field. Dorsal root ganglia were seeded in co-culture with Schwann cells on a substrate of polylactic acid microfibers coated with the electroconductive polymer polypyrrole, adding gold microfibers to increase its electrical conductivity. The substrate is capable of guiding axonal growth in a highly aligned manner and, when subjected to an electrical stimulation, an improvement in axonal growth is observed. As a result, an increase in the maximum length of the axons of 19.2% and an increase in the area occupied by the axons of 40% were obtained. In addition, an upregulation of the genes related to axon guidance, axogenesis, Schwann cells, proliferation and neurotrophins was observed for the electrically stimulated group. Therefore, our device is a good candidate for nerve regeneration therapies.

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“…To implement DRG feedback in a walking device, decoded predictions of hind limb state must be calculated in real time. Recently, limb locations were predicted from DRG recordings in real time and used to control intramuscular stimulation patterns in cats (Bauman et al , 2011; Bruns et al , 2013). The algorithm moved the leg sequentially to one of four quadrants based on predicted limb location.…”

Section: Introductionmentioning

confidence: 99%

Real-time control of walking using recordings from dorsal root ganglia

et al. 2013

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Objective The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components. Approach In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the dorsal root ganglia. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modeled from recorded neural firing rates. These models were then used for closed-loop feedback. Main Results Overall, firing-rate based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48±13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt. Significance Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development.

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Online feedback control of functional electrical stimulation using dorsal root ganglia recordings

Cited by 5 publications

References 12 publications

Real-time control of hind limb functional electrical stimulation using feedback from dorsal root ganglia recordings

Real-time control of hind limb functional electrical stimulation using feedback from dorsal root ganglia recordings

Electrical Stimulation Increases Axonal Growth from Dorsal Root Ganglia Co-Cultured with Schwann Cells in Highly Aligned PLA-PPy-Au Microfiber Substrates

Real-time control of walking using recordings from dorsal root ganglia

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