This second part of our work presents a comprehensive experimental evaluation and comparative analysis of a novel compact and cost-effective electromyographic (EMG) acquisition system, whose foundational design and theoretical principles are detailed in the first part. This work rigorously assesses a dual-channel prototype featuring both fully analog (Variant A) and hybrid analog-digital filtering (Variant B) designs. The system demonstrates robust binary grasp detection, achieving a mean latency of 230 ms, coupled with remarkable detection accuracy. Furthermore, this work critically compares our approach with contemporary, more complex neuroprosthetic interfaces, highlighting its unique advantages for rapid prototyping, educational purposes, and applications in resource-constrained environments. We extend the discussion to explore cutting-edge advancements in EMG signal processing techniques, the transformative role of machine learning integration for enhanced control, multimodal feedback systems, and the ongoing challenges in achieving robust, real-world clinical translation for advanced prosthetic control.