FPGA‐Based Real‐Time Implementation of Closed‐Loop Brain‐Controlled Eye Movement System
Guodao Zhang, Yupeng Li, Yanjie Lu, Genfu Yang, Xiaotian Pan, Jing Wang · CAAI Transactions on Intelligence Technology · 2026
ABSTRACT This paper presents a comprehensive closed‐loop brain‐eye movement framework, spanning parametric modelling, dynamic analysis and real‐time FPGA implementation. A simplified first‐order oculomotor model was derived from the classical Robinson formulation to preserve the essential neural‐mechanical feedback structure while enabling efficient hardware realization. Through systematic parametric variations of neural and mechanical properties and multiple classes of brain‐generated inputs (step, ramp, sinusoidal and noisy), the system exhibited stable closed‐loop behaviour with fast convergence and low steady‐state errors. Time‐domain and phase‐plane analyses confirmed that the eye accurately tracked diverse target trajectories across wide parameter ranges, with all equilibrium points classified as asymptotically stable. The discrete‐time model was implemented on a Xilinx Virtex‐4 FPGA using only adders, subtractors and shifters, completely avoiding multipliers. The design achieved a maximum clock frequency of 118 MHz, operated at 100 MHz with a closed‐loop latency of nine cycles (90 ns), and consumed approximately 280 mW total power while utilising less than 4% of the device resources. To validate the system in a practical setting, a real‐time hardware platform with a 3 × 3 target grid (nine fixation points) was developed. Experimental results demonstrated that the closed‐loop brain‐eye architecture reliably steered the eye towards each target with minimal tracking error and smooth dynamic response. Overall, this work bridges physiological oculomotor models and neuromorphic hardware, offering a scalable resource‐efficient platform for real‐time brain‐eye closed‐loop emulation and control.