Harnessing Field-Programmable Gate Arrays and Approximate Computing for Efficient MEA Signal Processing in Neuroscience

This study aims to enhance neural signal processing systems for neuroscience applications, focusing on efficient handling of data from microelectrode arrays (MEAs). MEAs are critical for recording neural activity, and their effectiveness depends on advanced signal processing for tasks like amplification, digitization, filtering and spike detection. This paper surveys existing methodologies based on personal computers, analog electronics, microcontrollers and field-programmable gate arrays (FPGAs). We highlight FPGAs as a promising technology due to their parallel processing capabilities, low latency, energy efficiency and adaptability for high-density MEA applications. Comparative analyses show that FPGA-based systems achieve sub-millisecond latency ([Formula: see text] [Formula: see text]ms) and power reductions of up to 30% using approximate computing techniques. For example, using reduced-precision filters (16-bit versus 32-bit) improves processing speed by 42% with [Formula: see text]% accuracy loss, while low-power multipliers cut dynamic power by 27%. These findings underscore the potential of FPGA-based approximate computing to deliver real-time, energy-efficient neural data processing. The integration of machine learning further enhances spike sorting and decoding accuracy. Future work will refine these techniques for clinical and neuroprosthetic applications, advancing brain–computer interfaces and personalized medicine.