Publication:   Journal of Multiscale Neuroscience                DOI:  https://doi.org/10.56280/1702827275

 

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.

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Keyword:  brain connectivity, signal processing, neuroscience, Machine Learning, EEG, MEG

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Conflict of Interest

The authors declare no conflict of interest

                
​Copyright: © 2025 The Author(s). Published by Neural Press.

This is an open access article distributed under the terms and conditions of the CC BY 4.0 license.

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Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, Neural Press™  or the editors, and the reviewers. Any product that may be evaluated in this article, or claim that made by its manufacturer, is not guaranteed or endorsed by the publisher.

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