Finite Element Model Capacitance for Neural Stimulation

The patent changes the fundamental parameters of the model by incorporating time-dependent capacitance effects and using realistic stimulus waveforms (asymmetric biphasic pulses) instead of static electrostatic assumptions. This transforms the model from a simplified DC-based approach to a dynamic AC-based approach that accounts for electrode and tissue capacitance, thereby improving prediction accuracy without excessive complexity increase

The patent replaces the electrostatic mechanical analogy with an electrical circuit model that explicitly includes capacitance elements. By substituting the purely conductive tissue model with a capacitive-resistive model that mirrors actual electrode-tissue interface physics, the model achieves better accuracy in predicting activation volumes while maintaining computational tractability through established electrical modeling techniques

The patent introduces dynamic elements by modeling the time-varying behavior of voltage-controlled stimulators with asymmetric biphasic waveforms. The model captures the transient capacitive charging and discharging phases, as well as the asymmetric nature of clinical waveforms, allowing accurate prediction of how these dynamic stimulation patterns actually activate neural tissue compared to static electrostatic assumptions

The patent incorporates periodic stimulation patterns with specific pulse widths and inter-pulse intervals that match clinical protocols. By modeling the periodic asymmetric biphasic waveforms with their characteristic cathodic and anodic phases, the model accurately predicts neural activation during realistic therapeutic stimulation cycles rather than continuous static fields