Artificial Intelligence for EEG Analysis: A Comprehensive Review of Deep Learning Techniques

This review systematically outlines the progression and implementation of artificial intelligence (AI) techniques, with a focus on deep learning (DL), for the analysis of electroencephalogram (EEG) signals. We cover the whole EEG processing pipeline from signal acquisition and preprocessing to feature extraction and classification. The survey begins with traditional machine learning techniques such as Common Spatial Patterns (CSP) and Support Vector Machines (SVMs), then transitions to more recent DL architectures—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their hybrid configurations—that have significantly expanded the analytical capabilities of neurophysiological systems. These methods have been widely applied in fields ranging from clinical diagnostics and brain-computer interaction to affective computing and cognitive assessment. Rather than relying on a singular methodology, research efforts emphasize tailored strategies to overcome persistent obstacles like signal denoising and the selection of meaningful features. We discuss applications in a broad array of areas like clinical diagnosis, brain-computer interfaces, emotion recognition, and cognitive tests. We compare performance metrics across methods and note existing limitations like interpretability issues and computational complexity. Finally, we mention future directions and trends, including multimodal integration and explainable AI. This review provides researchers and practitioners with a comprehensive overview of state-of-the-art AI techniques for EEG analysis and indicates some promising avenues for future advances in this rapidly evolving field.