Efficient search and fit methods to find nerve stimulation parameters for multi-contact electrodes

Freeberg, Max; Schiefer, Matthew A.; Triolo, Ronald J.

doi:10.1109/iembs.2011.6091829

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…During PSM, the pulse width was varied between 1 and 255 μs and the amplitude was varied between 0.1 and 5 mA. Modulation proceeded according to an adaptive gradient search in which an algorithm compared the EMG responses at spatially nearby stimuli [52]. If the difference in EMG responses was greater than a set threshold, a new stimulus was added at the midpoint between the two stimulus levels.…”

Section: Pulse Space Modulationmentioning

confidence: 99%

Selective activation of the human tibial and common peroneal nerves with a flat interface nerve electrode

Schiefer¹,

Freeberg²,

Pinault³

et al. 2013

J. Neural Eng.

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Problem Addressed Electrical stimulation has been shown effective in restoring basic lower extremity motor function in individuals with paralysis. We tested the hypothesis that a Flat Interface Nerve Electrode (FINE) placed around the human tibial or common peroneal nerve above the knee can selectively activate each of the most important muscles these nerves innervate for use in a neuroprosthesis to control ankle motion. Methodology During intraoperative trials involving three subjects, an 8-contact FINE was placed around the tibial and/or common peroneal nerve, proximal to the popliteal fossa. The FINE’s ability to selectively recruit muscles innervated by these nerves was assessed. Data were used to estimate the potential to restore active plantarflexion or dorsiflexion while balancing inversion and eversion using a biomechanical simulation. Results, Significance, and Potential Impact With minimal spillover to non-targets, at least three of the four targets in the tibial nerve, including two of the three muscles constituting the triceps surae were independently and selectively recruited in all subjects. As acceptable levels of spillover increased, recruitment of the target muscles increased. Selective activation of muscles innervated by the peroneal nerve was more challenging. Estimated joint moments suggests that plantarflexion sufficient for propulsion during stance phase of gait and dorsiflexion sufficient to prevent foot drop during swing can be achieved, accompanied by a small but tolerable inversion or eversion moment.

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Section: Pulse Space Modulationmentioning

confidence: 99%

Selective activation of the human tibial and common peroneal nerves with a flat interface nerve electrode

Schiefer¹,

Freeberg²,

Pinault³

et al. 2013

J. Neural Eng.

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show abstract

“…The binary search algorithm efficiently finds this threshold by iteratively narrowing a threshold-containing amplitude window until the desired precision is reached. This algorithm has been used to this end in several related studies [14,31,32]. A single pulse-spike pair was used to determine whether an axon became activated due to stimulation, and our pulses were exclusively monophasic cathodic.…”

Section: Axon Modelsmentioning

confidence: 99%

Artificial neural network-based rapid predictor of biological nerve fiber activation for DBS applications

et al. 2023

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Objective. Computational models are powerful tools that can enable the optimization of deep brain stimulation (DBS). To enhance the clinical practicality of these models, their computational expense and required technical expertise must be minimized. An important aspect of DBS models is the prediction of neural activation in response to electrical stimulation. Existing rapid predictors of activation simplify implementation and reduce prediction runtime, but at the expense of accuracy. We sought to address this issue by leveraging the speed and generalization abilities of artificial neural networks (ANNs) to create a novel predictor of neural fiber activation in response to DBS. Approach. We developed four variations of an ANN-based predictor to predict the response of individual, myelinated axons to extracellular electrical stimulation. ANNs were trained using datasets generated from a finite-element model (FEM) of an implanted DBS system together with multi-compartment cable models of axons. We evaluated the ANN-based predictors using three white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths. Main results. The top-performing ANN could predict the thresholds of axons with a mean absolute error (MAE) of 0.037 V, and pathway recruitment with an MAE of 0.079%, across all parameters. The ANNs reduced the time required to predict the thresholds of 288 axons by three to four orders of magnitude when compared to multi-compartment cable models. Significance. We demonstrated that ANNs can be fast, accurate, and robust predictors of neural activation in response to deep brain stimulation.

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Efficient search and fit methods to find nerve stimulation parameters for multi-contact electrodes

Cited by 2 publications

References 5 publications

Selective activation of the human tibial and common peroneal nerves with a flat interface nerve electrode

Selective activation of the human tibial and common peroneal nerves with a flat interface nerve electrode

Artificial neural network-based rapid predictor of biological nerve fiber activation for DBS applications

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