Advanced ML, DL, and Optimization Techniques for EEG Signal Analysis

This chapter presents a comprehensive overview of how Machine Learning (ML), Deep Learning (DL) and Optimization (OPT) techniques are reshaping EEG signal analysis. The chapter systematically explains the foundational blocks of EEG signal processing like signal acquisition, preprocessing, feature extraction, classification, and application and illustrates how AI is integrated at each stage to enhance the precision, scalability, and real-time performance. This chapter covers both, the fundamental principles and advanced developments in EEG-based intelligent systems. It covers the full flow from preprocessing techniques like noise reduction to sophisticated classification models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Transformers, and Graph Neural Networks (GNNs). The role of optimization methods, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Bayesian optimization, is also examined in the context of feature selection, hyperparameter tuning, and overall model performance enhancement.