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An estimated 22.3 million people worldwide had upper-limb amputations due to traumatic causes in 2017, representing nearly half of all such amputees. The need for advanced EMG-based prosthetic arms is growing; however, these devices typically rely on high-density electrode arrays and commercial-grade equipment, significantly limiting accessibility due to their high cost and maintenance requirements. Methods: Here, we show that affordable prosthetic control can be achieved using a limited number of electrodes from low-cost, commercial EMG hardware. Specifically, we used the Bitalino MuscleBIT™ EMG acquisition system to control a 3D-printed prosthetic hand adapted from the open-source e-NABLE Phoenix V3 design. We trained a lightweight multilayer perceptron that classified five distinct hand gestures from EMG signals recorded from three participants across three sessions each. Results: The system successfully classified five distinct hand gestures with 94.0% classification accuracy within subject on average and 83.3% classification accuracy across subjects. We demonstrate how sparse electrode configurations—using just four electrodes instead of high-density arrays—combined with open-source machine learning algorithms can achieve practical gesture recognition performance. Conclusions: This approach substantially reduces system cost and complexity while maintaining functional accuracy, enabling the development of low-cost EMG-controlled prosthetics for users unable to afford high-end commercial solutions. With a total system cost of under $300, this approach represents up to a 200x cost reduction compared to commercial myoelectric prostheses while maintaining comparable classification performance.