Brain–Computer Interface (BCI) technology has undergone a transformative evolution in recent years, enabling increasingly intuitive and direct control of prosthetic devices. This research paper introduces a novel system that leverages Electroencephalography (EEG) signals for prosthetic hand control by extracting salient motor-related brainwave frequencies. The system’s control algorithm is based on the established roles of the beta (13–30 Hz) and gamma (30–100 Hz) frequency bands in motor planning and execution; specifically, it detects spikes in beta activity to trigger state transitions in the prosthetic hand. Non-invasive EEG electrodes were applied to the subject’s scalp to gather EEG data, and signal processing methods were used to separate the beta and gamma bands. A dynamic threshold was established to identify actionable neural patterns. Each instance of beta wave amplitude exceeding this threshold resulted in a change in the hand’s state, from open to closed or vice versa. Through real-time processing and a practical testing configuration, the system achieved a response time of 500 ms and an accuracy rate of 85%, thereby demonstrating its potential for real-world application. In addition to confirming the viability of EEG-based prosthetic control, this study clarifies its inherent advantages and disadvantages, such as the requirement for user training and the difficulty of signal variability. The findings augment the rapidly growing corpus of research on non-invasive BCIs, advancing the development of more accessible and patient-centric prosthetic solutions that promote mobility and independence.