A Survey of Methods and Architectures for Design of Reconfigurable Low Noise Amplifier using Hybrid Forensic-Based Investigation Algorithm and Human Urbanization Algorithm for EEG Classification

Abstract

Electroencephalogram (EEG) signal classification plays a vital role in biomedical applications such as brain–computer interfaces, neurological disorder diagnosis, and cognitive monitoring systems. However, EEG signals are inherently low-amplitude and highly susceptible to noise, making accurate acquisition and processing a challenging task. This paper presents a comprehensive survey of methods and architectures for designing reconfigurable low-noise amplifiers (LNAs) integrated with hybrid forensic-based investigation algorithms and human urbanization algorithms for EEG classification. Reconfigurable LNAs enable adaptive gain control, improved signal-to-noise ratio (SNR), and reduced power consumption, which are critical for wearable and real-time EEG systems. Deep learning techniques, particularly convolutional neural networks (CNNs) and hybrid models such as CNN-LSTM and CNN-GRU, have demonstrated superior performance in extracting spatial and temporal features from EEG signals. Furthermore, hybrid optimization algorithms improve feature selection, reduce redundancy, and enhance classification accuracy while minimizing computational complexity. The integration of hardware-efficient LNA design with advanced deep learning and optimization techniques leads to improved performance, reliability, and energy efficiency. This survey highlights recent advancements, identifies key challenges, and provides future research directions for developing adaptive and high-performance EEG classification systems.