Bio-Signal Activated FPGA-Based System for Robotic-Assisted Rehabilitation

This paper presents a pivotal contribution to multi-modal bio-signal processing for robotic control applications, leveraging cutting-edge Field Programmable Gate Array (FPGA) technology to unlock new frontiers in accuracy, efficiency, and real-time control. The comprehensive hardware implementation strategy is characterized by the strategic utilization of a Fast Fourier Transform/ Inverse Fast Fourier Transform (FFT/IFFT) - based filtering algorithm optimized for Electroencephalogra-phy (EEG) signal processing. Concurrently, an inventive movement classification methodology powered by Electrooculography (EOG) signals is introduced. The core proposition of the paper revolves around the implementation of an EEG and EOG-driven Multi Input Single Output (MISO) control system, intended for the control of rehabilitation robots. The effectiveness of the proposed method was verified by simulation in Lab VIEW. The algorithm has been seamlessly integrated into the My Rio 1900 hardware platform, leveraging the immense processing power of a Xilinx FPGA.