Energy and Area-Efficient FIR Filter Architecture for Low-Power EEG Signal Processing

In order to process any signal efficiently for more realistic brain activity analysis with least power consumption and simplicity of hardware, digital encephalography systems are definitely required. This paper describes an optimized FIR filter architecture designed to improve power efficiency and reduce silicon area, which should make it suitable for real-time electroencephalography applications. It makes use of coefficient optimization, resource-sharing strategies, and lowpower arithmetic units designed to achieve considerable reduction in computational complexity. It uses the architecture to integrate hardware-efficient multipliers, advanced pipelining, and approximate computing techniques, which help to improve the speed and energy efficiency without affecting signal fidelity. Power consumption, area utilization, and processing speed for FPGA and ASIC implementations of the proposed FIR filter were tested and validated. The experimental results have shown that it consumes 40% less power and uses 30% less area compared with conventional FIR filter designs. In addition, the architecture enables adaptive filtering for real-time noise cancellation, thereby enhancing the quality of EEG signal acquisition in biomedical applications. The work contributes to the advancement of digital encephalography systems by offering a scalable and energy-efficient filtering solution suitable for next-generation brain-computer interfaces and portable EEG monitoring devices.