Artificial Intelligence Techniques for Design of Reconfigurable Low Noise Amplifier using Hybrid Forensic-Based Investigation Algorithm and Human Urbanization Algorithm for EEG Classification: Trends and Challenges

Abstract

Electroencephalogram (EEG) signal classification has become a crucial component in modern biomedical systems, particularly in brain–computer interfaces (BCIs), neurological disorder diagnosis, and cognitive monitoring applications. However, EEG signals are characterized by low amplitude and high susceptibility to noise, making accurate acquisition and processing challenging. This paper presents a comprehensive review of artificial intelligence techniques for the design of reconfigurable low-noise amplifiers (LNAs) integrated with hybrid forensic-based investigation algorithms and human urbanization algorithms for EEG classification. Reconfigurable LNAs enable adaptive gain, bandwidth tuning, and improved signal-to-noise ratio (SNR), which are essential for high-quality signal acquisition in wearable and real-time systems. Recent advancements in deep learning, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid architectures, have significantly improved EEG classification accuracy by extracting complex spatial and temporal features. Additionally, hybrid optimization algorithms enhance feature selection and reduce computational complexity, leading to improved system efficiency. The integration of AI techniques with hardware-efficient LNA design enables robust, low-power, and high-performance EEG systems. This paper also highlights emerging trends, key challenges, and future research directions for developing adaptive and intelligent EEG classification frameworks.