Comparison of Artificial Intelligence Applications of EEG Signals in Neuroscience

Abstract

In recent years, there has been a growing interest in the artificial intelligence (AI)-based analysis of electroencephalography (EEG) signals. This surge has made the potential of EEG more evident, both in monitoring cognitive states and in the early diagnosis of neurological disorders. This review systematically evaluates the academic literature from the past decade focusing on the processing of EEG signals through machine learning (ML), deep learning (DL), and other alternative techniques. The study compares personalized ML models (e.g., SVM, Random Forest) with wavelet decomposition–based optimized approaches and further analyzes the performance of Hilbert transform–based Convolutional Neural Network (CNN) architectures, label-free autoencoder frameworks, and multi-architecture DL systems in contemporary brain–computer interface (BCI) applications. In addition, incremental learning models based on multimodal data fusion are reviewed in the context of diagnosing disorders such as Alzheimer’s disease and epilepsy. The findings indicate that EEG–AI integration holds substantial potential for both research and clinical applications.