A high-performance ultra-wideband (UWB) radio frequency (RF) front-end architecture that is inherently designed with a capability to acquire brain signals in real-time in next-generation neuroprosthetic systems is presented, developed, and experimentally validated in this paper. As more wireless brain machine interfaces (BMI) and closed-loop neuroprosthetic devices become clinically useful, the RF front-end circuitry has been a critical need in support of transmission of low-amplitude, high-resolution neural activity; such as intracranial electroencephalography (iEEG) and local field potentials (LFPs) with minimal delays, high fidelity and ultra-low power requirements. The suggested front-end consists of an integrated-global positioning system (GPS) front-end architecture that is miniaturized and fully integrated including a low-noise amplifier (LNA), high-Q bandpass filter, passively conceived frequency mixer with low-phase-noise local oscillator (LO) and high-rate analog-to-digital conversion interface. The system is designed based on sophisticated 65 nm CMOS production technology and is working effectively at an operating frequency of up to 0.1-6GHz UWB and optimized in terms of implantable or wearable models. Front-end circuit-level Simulation and analysis range conjugated with Cadence and ADS determine a gain of 18.5 dB, noise figure of less than 1.2 dB and a 2.3 mW power consumption with an end-to-end latency of less than 5 microseconds, assured within the safe limits of biomedical applications. Co-simulation of electromagnetic in CST also verifies the validity of the RF signal pathway in realistic layout and packaging parasitics. A prototype was implemented in proof-of-concept cutting edge to test on synthetic brain signal emulators and wireless transmission systems having a robust performance with signal recovery below millivolts and minimum bit error rate (BER) in the context of mobility induced fading. Its high bandwidth, power, and signal integrity make the system a very promising technique in future consideration of implantable neural recording and stimulation systems. The present research establishes a basis of scalable, low-latency, and interference-tolerant RF telemetry in braincomputer interface living and paves the way to smart neuromodulation systems that provide real-time feedback.
