Review on design of a Cross-System Neural Filter for Processing of Brain Signals using AI

Brain signal processing plays a crucial role in advancing brain-computer interfaces (BCIs), neuroprosthetics, and cognitive health monitoring. However, traditional signal processing techniques frequently suffer from low accuracy due to noise, artifact interference, and non-stationarity of neural signals like EEG/MEG. This paper proposes a Cross-System Neural Filter (CSNF) — an integrated, AI-driven framework leveraging advanced machine learning, deep learning, and data science methodologies to enhance neural signal quality and extract meaningful features across multiple brain-signal recording systems. We evaluate the CSNF against conventional filters using real EEG datasets and demonstrate statistically significant improvements in signal clarity and classification accuracy. Results indicate CSNF’s potential for improving real-time neurodata processing and adaptive BCI performance.