Recent Advances in Design of Reconfigurable Low Noise Amplifier using Hybrid Forensic-Based Investigation Algorithm and Human Urbanization Algorithm for EEG Classification: A Systematic Review

Abstract

Low Noise Amplifiers (LNAs) are critical components in biomedical signal acquisition systems, particularly in electroencephalography (EEG), where weak neural signals require amplification with minimal noise distortion. LNAs enhance signal strength while preserving signal-to-noise ratio (SNR), which is essential for accurate EEG classification. Recent advancements in reconfigurable LNA design focus on improving gain, noise figure, and power efficiency through adaptive architectures and optimization algorithms. Emerging artificial intelligence (AI)-based optimization techniques, such as hybrid forensic-based investigation algorithms and human urbanization algorithms, have been applied to enhance LNA design parameters. These algorithms enable dynamic tuning of circuit elements, improving noise performance, bandwidth, and energy efficiency. Additionally, reconfigurable feedback and load networks have been introduced to achieve wideband operation and maintain low noise figures across multiple frequency bands. Recent research highlights the importance of CMOS-based reconfigurable LNAs for biomedical applications, offering compact design, low power consumption, and adaptability for multi-band signal processing. This systematic review analyses recent advancements in reconfigurable LNA design integrated with AI optimization techniques for EEG classification. It identifies key trends, evaluates performance improvements, and discusses challenges such as noise optimization, hardware complexity, and scalability