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

Area of Science:

• Biomedical Engineering
• Neuroscience
• Signal Processing

Background:

• Electromyography (EMG) recruitment curves are crucial for understanding nerve activation.
• Efficiently sampling EMG recruitment space is challenging due to the wide range of stimulation parameters.
• Current methods may not adequately explore the complex relationship between stimulation parameters and muscle response.

Purpose of the Study:

• To develop and validate an efficient method for sampling EMG recruitment space.
• To identify high-information areas within the recruitment surface using a gradient-based search.
• To fit Gompertz-function-inspired surfaces to EMG recruitment data for parameter determination.

Main Methods:

• A gradient-based search algorithm was developed to efficiently navigate the EMG recruitment space.
• The method was initially tested using simulated EMG recruitment data.
• The search method was applied to determine stimulation parameters using an 8-contact flat interface nerve electrode (FINE).

Main Results:

• The gradient-based search effectively sampled simulated EMG recruitment data.
• Gompertz-function-inspired surfaces were successfully fitted to the sampled data.
• The method demonstrated its ability to determine stimulation parameters for the FINE electrode.

Conclusions:

• The developed gradient-based search method provides an efficient approach to sample EMG recruitment space.
• The Gompertz surface fitting method is validated for analyzing EMG recruitment data.
• This approach facilitates optimized parameter determination for nerve stimulation applications.