Brain-Implant System for Real-Time Control of Prosthetic Limbs with Integrated Sensory Feedback and Machine Learning Adaptation

This research presents the design and conceptual development of an advanced brain-implant system for real-time control of prosthetic limbs with integrated sensory feedback and machine learning adaptation. The proposed system enables bi-directional communication between the human brain and a smart prosthetic limb, allowing precise motor control while restoring the sense of touch and temperature perception. Neural signals are acquired via a cortical implant, decoded using adaptive machine learning algorithms, and transmitted to the prosthetic limb for actuation. Embedded sensors in the prosthetic provide tactile and thermal feedback, which is relayed back to the brain through targeted neural stimulation. This bidirectional loop aims to enhance embodiment, reduce latency, and improve long-term user adaptation. The study reviews current brain–computer interface technologies, identifies existing limitations, and proposes a novel architecture that integrates advanced neural signal processing, low-power implant design, and seamless wireless communication, paving the way for next-generation assistive technologies in neuroprosthetics.