Systematic analysis of optimal design of EEG signal processing circuits

Electroencephalography (EEG) technology provides an important tool for neuroscience research and clinical diagnosis by recording electrical activity in the cerebral cortex. However, EEG signal processing faces several technical challenges, including signal interference, real-time processing requirements, and low-power design, especially in portable and wearable devices. In addition, the nonlinear and time-varying characteristics of EEG signal further increase the difficulty of processing. To address these challenges, the design and optimization methods of EEG signal acquisition and processing circuits are discussed in detail, and schemes such as advanced signal processing technology, modular design and high dynamic range amplifier are proposed to improve signal quality and system performance. With the development of artificial intelligence and machine learning technology, EEG signal processing will show broad application prospects in the fields of brain-computer interface and multi-biological signal fusion. The results of this study provide theoretical basis and practical guidance for further improving the design efficiency and application potential of EEG system.