D
Dimitris Kugiumtzis
Researcher at Aristotle University of Thessaloniki
Publications - 130
Citations - 3684
Dimitris Kugiumtzis is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Granger causality & Transfer entropy. The author has an hindex of 31, co-authored 119 publications receiving 3084 citations. Previous affiliations of Dimitris Kugiumtzis include University of Oslo & University of Amsterdam.
Papers
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Journal ArticleDOI
State Space Reconstruction Parameters in the Analysis of Chaotic Time Series - the Role of the Time Window Length
TL;DR: The most common state space reconstruction method in the analysis of chaotic time series is the Method of Delays (MOD), and many techniques have been suggested to estimate the parameters of MOD, i.e., the time delay and the embedding dimension $m$ as mentioned in this paper.
Journal ArticleDOI
State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length
TL;DR: The most common state space reconstruction method in the analysis of chaotic time series is the Method of Delays (MOD), and many techniques have been suggested to estimate the parameters of MOD, i.e., the time delay τ and the embedding dimension m.
Journal ArticleDOI
Clinical utility and prospective of TMS–EEG
Sara Tremblay,Sara Tremblay,Nigel C. Rogasch,Isabella Premoli,Daniel M. Blumberger,Silvia Casarotto,Robert Chen,Vincenzo Di Lazzaro,Faranak Farzan,Fabio Ferrarelli,Paul B. Fitzgerald,Paul B. Fitzgerald,Jeanette Hui,Risto J. Ilmoniemi,Vasilios K. Kimiskidis,Dimitris Kugiumtzis,Pantelis Lioumis,Alvaro Pascual-Leone,Maria Concetta Pellicciari,Tarek K. Rajji,Gregor Thut,Reza Zomorrodi,Ulf Ziemann,Zafiris J. Daskalakis +23 more
TL;DR: A comprehensive review of studies that have used TMS-EEG in clinical populations and to discuss potential clinical applications is presented, including recommendations for how to address some of the salient challenges faced in clinical TMS -EEG research.
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Nonuniform state-space reconstruction and coupling detection
TL;DR: This work investigates in records of scalp epileptic EEG the information flow across brain areas and proposes a method for building embedding vectors progressively using information measure criteria regarding past, current, and future states.
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Direct-coupling information measure from nonuniform embedding.
TL;DR: It is shown that PMIME detects correctly direct coupling and outperforms the (linear) conditional Granger causality and the partial transfer entropy, and does not rely on significance test and embedding parameters.