Analysis of power losses and the efficacy of power minimization strategies in multichannel electrical stimulation systems

Abstract Objective. Neuroprosthetic devices require multichannel stimulator systems with an increasing number of channels. However, there are inherent power losses in typical multichannel stimulation circuits caused by mismatches between the power supply voltage and the voltage required at each electrode to successfully stimulate tissue. This imposes a bottleneck towards high-channel-count devices, which is particularly severe in wirelessly-powered devices. Hence, advances in the power efficiency of stimulation systems are critical. To support these advances, this paper presents a methodology to identify and quantify power losses associated with different power supply scaling strategies in multichannel stimulation systems. Approach. The methodology uses distributions of stimulation amplitudes and electrode impedances to calculate power losses in multichannel systems. Experimental data from prior studies spanning various stimulation applications were analyzed to evaluate the performance of fixed, global, and stepped supply scaling methods, focusing on their impact on power dissipation and efficiency. Main Results. Variability in output conditions results in low power efficiency in multichannel stimulation systems across all applications. Stepped voltage scaling demonstrates substantial efficiency improvements, achieving an increase of 43% to 100%, particularly in high-channel-count applications with significant variability in tissue impedance. In contrast, global scaling proved effective only in systems with fewer channels and minimal inter-channel variation. Significance. The findings highlight the importance of tailoring power management strategies to specific applications to optimize efficiency while minimizing system complexity. The proposed methodology provides a framework for evaluating trade-offs between efficiency and system complexity, facilitating the design of more scalable and power-efficient neurostimulation systems.