Advanced search
3 files | 863.06 KB

Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches

Margarita Papadopoulou (UGent) , Kristl Vonck (UGent) , Paul Boon (UGent) and Daniele Marinazzo (UGent)
Author
Organization
Project
The integrative neuroscience of behavioral control (Neuroscience)
Abstract
Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient.
Keywords
TIME-SERIES, SPATIOTEMPORAL DYNAMICS, ELECTRICAL-ACTIVITY, SEIZURE PREDICTION, COHERENCE, GRANGER CAUSALITY, NETWORKS, INFORMATION-FLOW, TEMPORAL-LOBE EPILEPSY, DIRECTED TRANSFER-FUNCTION

Downloads

  • dsfs 3060256.txt
    • data factsheet
    • |
    • open access
    • |
    • text/x-matlab
    • |
    • 3.56 KB
  • paper conn epilepsy.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 295.12 KB
  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 564.39 KB

Citation

Please use this url to cite or link to this publication:

Chicago
Papadopoulou, Margarita, Kristl Vonck, Paul Boon, and Daniele Marinazzo. 2012. “Mapping the Epileptic Brain with EEG Dynamical Connectivity: Established Methods and Novel Approaches.” European Physical Journal Plus 127 (11).
APA
Papadopoulou, M., Vonck, K., Boon, P., & Marinazzo, D. (2012). Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches. EUROPEAN PHYSICAL JOURNAL PLUS, 127(11).
Vancouver
1.
Papadopoulou M, Vonck K, Boon P, Marinazzo D. Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches. EUROPEAN PHYSICAL JOURNAL PLUS. 2012;127(11).
MLA
Papadopoulou, Margarita, Kristl Vonck, Paul Boon, et al. “Mapping the Epileptic Brain with EEG Dynamical Connectivity: Established Methods and Novel Approaches.” EUROPEAN PHYSICAL JOURNAL PLUS 127.11 (2012): n. pag. Print.
@article{3060256,
  abstract     = {Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient.},
  articleno    = {144},
  author       = {Papadopoulou, Margarita and Vonck, Kristl and Boon, Paul and Marinazzo, Daniele},
  issn         = {2190-5444},
  journal      = {EUROPEAN PHYSICAL JOURNAL PLUS},
  keyword      = {TIME-SERIES,SPATIOTEMPORAL DYNAMICS,ELECTRICAL-ACTIVITY,SEIZURE PREDICTION,COHERENCE,GRANGER CAUSALITY,NETWORKS,INFORMATION-FLOW,TEMPORAL-LOBE EPILEPSY,DIRECTED TRANSFER-FUNCTION},
  language     = {eng},
  number       = {11},
  pages        = {13},
  title        = {Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches},
  url          = {http://dx.doi.org/10.1140/epjp/i2012-12144-5},
  volume       = {127},
  year         = {2012},
}

Altmetric
View in Altmetric
Web of Science
Times cited: