World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
50
Citations
8237
World Ranking
4140
National Ranking
115

Luca Faes publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Luca Faes sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 273 publications — 70th percentile

70% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Luca Faes D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Luca Faes sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 50 D-Index — 59th percentile

59% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Luca Faes is a researcher affiliated with the University of Palermo in Italy, engaged primarily in fields related to Medicine and Neuroscience. Their work spans various subfields including Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Biomedical Engineering, Statistical and Nonlinear Physics, and Economics and Econometrics.

The main topics addressed in Luca Faes's research cover a range of areas such as Heart Rate Variability and Autonomic Control, Functional Brain Connectivity Studies, Neural Dynamics and Brain Function, Non-Invasive Vital Sign Monitoring, EEG and Brain-Computer Interfaces, Complex Systems and Time Series Analysis, and Mental Health Research Topics.

Among recent publications, several notable papers authored or co-authored by Luca Faes include:

  • "A New Framework for the Time- and Frequency-Domain Assessment of High-Order Interactions in Networks of Random Processes" (2022) published in IEEE Transactions on Signal Processing
  • "Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends" (2023) published in Bioengineering (authored by Giovanni Chiarion with citation relations)
  • "Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks" (2020) published in Entropy (authored by Yuri Antonacci with citation relations)
  • "Multivariate Correlation Measures Reveal Structure and Strength of Brain-Body Physiological Networks at Rest and During Mental Stress" (2021) published in Frontiers in Neuroscience (authored by Riccardo Pernice with citation relations)
  • "An Information-Theoretic Framework to Measure the Dynamic Interaction between Neural Spike Trains" (2021) published by Nova Science Publishers (Nova Science Publishers, Inc.) (authored by Gorana Mijatović with citation relations)

Luca Faes frequently collaborates with a set of co-authors, building research connections in various topics. These frequent co-authors include:

  • Yuri Antonacci
  • Riccardo Pernice
  • Gorana Mijatović
  • Laura Sparacino
  • Chiara Barà

Their work has been published extensively across several venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Entropy
  • 2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE Transactions on Biomedical Engineering

Best Publications

  • Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique

    Luca Faes;Giandomenico Nollo;Alberto Porta

  • Surrogate data analysis for assessing the significance of the coherence function

    L. Faes;G.D. Pinna;A. Porta;R. Maestri

  • Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations.

    Wanting Xiong;Luca Faes;Plamen Ch. Ivanov

  • MuTE: A MATLAB Toolbox to Compare Established and Novel Estimators of the Multivariate Transfer Entropy

    Alessandro Montalto;Luca Faes;Daniele Marinazzo

  • An integrated approach based on uniform quantization for the evaluation of complexity of short-term heart period variability: Application to 24 h Holter recordings in healthy and heart failure humans.

    A. Porta;L. Faes;M. Masé;G. D’Addio

  • Critical Comments on EEG Sensor Space Dynamical Connectivity Analysis

    Frederik Van de Steen;Luca Faes;Esin Karahan;Jitkomut Songsiri

  • Wiener–Granger Causality in Network Physiology With Applications to Cardiovascular Control and Neuroscience

    Alberto Porta;Luca Faes

  • A method for quantifying atrial fibrillation organization based on wave-morphology similarity

    L. Faes;G. Nollo;R. Antolini;F. Gaita

  • Information Decomposition in Bivariate Systems: Theory and Application to Cardiorespiratory Dynamics

    Luca Faes;Alberto Porta;Giandomenico Nollo

  • Measuring Connectivity in Linear Multivariate Processes: Definitions, Interpretation, and Practical Analysis

    Luca Faes;Silvia Erla;Giandomenico Nollo

  • Linear and non-linear brain-heart and brain-brain interactions during sleep.

    Luca Faes;Daniele Marinazzo;Fabrice Jurysta;Giandomenico Nollo

  • Effect of age on complexity and causality of the cardiovascular control: comparison between model-based and model-free approaches.

    Alberto Porta;Luca Faes;Vlasta Bari;Andrea Marchi

  • Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks

    Luca Faes;Luca Faes;Alberto Porta;Giandomenico Nollo;Giandomenico Nollo;Michal Javorka

  • Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions

    Luca Faes;Giandomenico Nollo

  • Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series

    Luca Faes;Giandomenico Nollo;Alberto Porta

  • Mutual nonlinear prediction as a tool to evaluate coupling strength and directionality in bivariate time series: comparison among different strategies based on k nearest neighbors.

    Luca Faes;Alberto Porta;Giandomenico Nollo

  • Testing Frequency-Domain Causality in Multivariate Time Series

    L Faes;A Porta;G Nollo

  • Information dynamics of brain?heart physiological networks during sleep

    Luca L. Faes;Giandomenico G. Nollo;Fabrice Jurysta;Daniele D. Marinazzo

  • Estimating the decomposition of predictive information in multivariate systems

    Luca Faes;Dimitris Kugiumtzis;Giandomenico Nollo;Fabrice Jurysta

  • Mechanisms of causal interaction between short-term RR interval and systolic arterial pressure oscillations during orthostatic challenge

    Luca Faes;Giandomenico Nollo;Alberto Porta

  • Information domain approach to the investigation of cardio-vascular, cardio-pulmonary, and vasculo-pulmonary causal couplings.

    Luca Faes;Giandomenico Nollo;Alberto Porta

  • Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series

    Luca Faes;Giandomenico Nollo;Alberto Porta

  • A framework for assessing frequency domain causality in physiological time series with instantaneous effects.

    Luca Faes;Silvia Erla;Alberto Porta;Giandomenico Nollo;Giandomenico Nollo

Frequent Co-Authors

Giandomenico Nollo
Giandomenico Nollo University of Trento
Daniele Marinazzo
Daniele Marinazzo Ghent University
Ki H. Chon
Ki H. Chon University of Connecticut
Gaetano Valenza
Gaetano Valenza University of Pisa
Ludovico Minati
Ludovico Minati University of Trento
Enzo Pasquale Scilingo
Enzo Pasquale Scilingo University of Pisa
Laura Astolfi
Laura Astolfi Sapienza University of Rome
Mattia Frasca
Mattia Frasca University of Catania
Tjeerd W. Boonstra
Tjeerd W. Boonstra Maastricht University

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