Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG
Abstract
Brain connectivity can be modeled and quantified with a large number of techniques. The main objective of this paper is to present the most modern and widely established mathematical methods for calculating connectivity that is commonly applied to functional high resolution multichannel neurophysiological signals, including electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. A historical timeline of each technique is outlined along with some illustrative applications. The most crucial underlying assumptions of the presented methodologies are discussed in order to help the reader understand where each technique fits into the bigger picture of measuring brain connectivity. In this endeavor, linear, nonlinear, causality-assessing and information-based techniques are summarized in the framework of measuring functional and effective connectivity. Model based vs. data-driven techniques and bivariate vs. multivariate methods are also discussed. Finally, certain important caveats (i.e. stationarity assumption) pertaining to the applicability of the methods are also illustrated along with some examples of clinical applications.
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The spatial information carried by EEG signals plays an important role in AD/HC classification.
“The spatial information carried by EEG signals plays an important role in AD/HC classification (Babiloni et al., 2005; Sakkalis, 2011; Tafreshi et al., 2019; Van Mierlo et al., 2014).”
From <scp>Spatial–temporal</scp> graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram (2022) · cites this paper as Sakkalis, 2011
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879 · see citing works
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459 · see citing works
Ding, M., Bressler, S. L., Yang, W., & Liang, H. (2000) · Biological Cybernetics · doi:10.1007/s004229900137 · Cited by 582 · see citing works
Mutual information analysis of the EEG in patients with Alzheimer's disease
Jeong, J., Gore, J. C., & Peterson, B. S. (2001) · Clinical Neurophysiology · doi:10.1016/s1388-2457(01)00513-2 · Cited by 397 · see citing works
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Clinical utility and prospective of TMS–EEG
Tremblay, S., Rogasch, N. C., Premoli, I., Blumberger, D. M., Casarotto, S., Chen, R., Di Lazzaro, V., Farzan, F., Ferrarelli, F., Fitzgerald, P. B., et al. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.01.001 · Cited by 472 · see citing works
Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302 · see citing works
Shan, X., Cao, J., Huo, S., Chen, L., Sarrigiannis, P. G., & Zhao, Y. (2022) · Human Brain Mapping · doi:10.1002/hbm.25994 · Cited by 75 · see citing works
“The spatial information carried by EEG signals plays an important role in AD/HC classification (Babiloni et al., 2005; Sakkalis, 2011; Tafreshi et al., 2019; Van Mierlo et al., 2014).”
BackgroundMethodAdvances in Electrophysiological Research
Kamarajan, C. (2015) · Alcohol Research: Current Reviews · doi:10.35946/arcr.v37.1.05 · Cited by 72 · see citing works
Desjardins, M.-È., Carrier, J., Lina, J.-M., Fortin, M., Gosselin, N., Montplaisir, J., & Zadra, A. (2017) · Sleep · doi:10.1093/sleep/zsx024 · Cited by 56 · see citing works
- Authors
- Sakkalis, V.
- Affiliations
- Science and Technology Park of Crete (GR)
- Corresponding author
- Sakkalis, V.
- ORCID
- Sakkalis, V.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2011-12
- Volume
- 41
- Issue
- 12
- Pages
- 1110-1117
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2043729845
- Cited by
- 637
- References
- 4
- Citations by year
- 2026: 25 · 2025: 46 · 2024: 42 · 2023: 55 · 2022: 60 · 2021: 61 · 2020: 51 · 2019: 69 · 2018: 62 · 2017: 40 · 2016: 34 · 2015: 29 · 2014: 30 · 2013: 18 · 2012: 14
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- brain connectivity, functional connectivity, effective connectivity, EEG, MEG, causal analysis, stationarity assumption, clinical application
- MeSH terms
- Brain, Brain Mapping, Electroencephalography, Humans, Magnetoencephalography
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