Electroencephalography (EEG) has transitioned from a subjective observational method into a data-intensive analytical field that utilises sophisticated algorithms and mathematical models. This review provides a holistic foundation by detailing the neurophysiological basis, recording techniques, and applications of EEG before providing a rigorous examination of traditional and modern analytical pillars. Statistical and Time-Series Analysis, Spectral and Time-Frequency Analysis, Spatial Analysis and Source Modelling, Connectivity and Network Analysis, and Nonlinear and Chaotic Analysis are explored. Afterwards, while acknowledging the historical role of Machine Learning (ML) and Deep Learning (DL) architectures, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), this review shifts the primary focus toward current state-of-the-art Artificial Intelligence (AI) trends. We place emphasis on the emergence of Foundation Models, including Large Language Models (LLMs) and Large Vision Models (LVMs), adapted for high-dimensional neural sequences. Finally, we explore the integration of Generative AI for data augmentation and review Explainable AI (XAI) frameworks designed to bridge the gap between “black-box” decoding and clinical interpretability. We conclude that the next generation of EEG analysis will likely converge into Neuro-Symbolic architectures, synergising the massive generative power of foundation models with the rigorous, rule-based interpretability of classical signal theory.

Electroencephalography (EEG) has transitioned from a subjective observational method into a data-intensive analytical field that utilises sophisticated algorithms and mathematical modeling. This progression encompasses developments in signal preprocessing, artifact removal, and feature extraction techniques including time-domain, frequency-domain, time-frequency, and nonlinear complexity measures. To provide a holistic foundation for researchers, this review begins with the neurophysiological basis, recording technique and clinical applications of EEG, while maintaining its primary focus on the diverse methods used for signal analysis. It offers an overview of traditional mathematical techniques used in EEG analysis, alongside contemporary, state-of-the-art methodologies. Machine Learning (ML) and Deep Learning (DL) architectures, such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and transformer models, have been employed to automate feature learning and classification across diverse applications. We conclude that the next generation of EEG analysis will likely converge into Neuro-Symbolic architectures, synergising the generative power of foundation models with the rigorous interpretability of signal theory.