Time–Frequency Analysis and Machine Learning for Detecting Brain Oscillations in EEG Data

High-frequency oscillations (HFOs) in electroencephalography (EEG) signals serve as vital biomarkers for identifying epileptogenic zones. However, their transient nature and low amplitude make reliable detection challenging. This paper reviews and compares major time–frequency (TF) analysis methods—Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), and Hilbert–Huang Transform (HHT)—highlighting their role in enhancing temporal and spectral resolution for HFO detection. The study also explores the integration of machine learning (ML) techniques, including Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and hybrid deep learning models, to achieve automated and accurate detection. By combining TF analysis with ML frameworks, recent approaches demonstrate significant improvements in sensitivity, specificity, and clinical applicability. This review emphasizes how the fusion of advanced signal processing and data-driven algorithms can optimize EEG analysis workflows, supporting more reliable diagnosis and decision-making in epilepsy management.