Akademik Veri Yönetim Sistemi
Computers and Electronics in Medicine, cilt.3, sa.1, ss.11-26, 2026 (Hakemli Dergi)
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.