High performance FIR Architecture for EOG Signal Noise Supression

A unique architecture for a Finite Impulse Response (FIR) filter is proposed in this study to efficiently reduce noise in Electrooculography (EOG) signals. Electromagnetic interference (EMI) and muscular activity are two common sources of noise that regularly alter EOG signals, which are utilized to identify eye movements. Real-time applications can be challenging for conventional FIR filter architectures because of their restrictions on power consumption and delay. By utilizing a state-of-the-art technique for coefficient quantization that reduces the number of multipliers required in the filter, the recommended architecture gets around these shortcomings. FPGA implementation leverages the reconfigurability and parallel processing capabilities of FPGAs to achieve efficient denoising. The systolic FIR filter is implemented on an FPGA platform to achieve real-time denoising of EOG signals. The systolic architecture allows for efficient parallel processing of the input signal samples, resulting in high throughput and low latency. The implementation is verified using EOG signal datasets, and the denoising performance is evaluated in terms of signal-to-noise ratio (SNR) and mean square error (MSE).