Abstract

Understanding brain connectivity is crucial for deciphering both neural function and dysfunction. This review highlights the key signal-processing methods used to analyze brain connectivity, including techniques such as Fourier transforms, wavelet analysis, and graph-theoretical approaches. Applications of these techniques across neuroimaging and electrophysiological modalities such as electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) are examined. Additionally, challenges such as noise reduction, signal non-stationarity, and computational complexity are addressed. By bridging neuroscience and signal processing, this review aims to provide insights into the strengths and limitations of both traditional and cutting-edge signal-processing methods for studying brain connectivity while also highlighting potential future research directions.