D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 35 Citations 4,156 92 World Ranking 3500 National Ranking 142
Neuroscience D-index 31 Citations 3,548 84 World Ranking 5152 National Ranking 305

Overview

What is he best known for?

The fields of study he is best known for:

  • Quantum mechanics
  • Artificial intelligence
  • Machine learning

The scientist’s investigation covers issues in Artificial intelligence, Electroencephalography, Pattern recognition, Neuroscience and Wavelet. His Artificial intelligence study combines topics in areas such as Ripple, Machine learning, Computer vision and Masking. His Electroencephalography research includes elements of Temporal lobe and Default mode network.

His studies in Pattern recognition integrate themes in fields like Inverse problem, Principle of maximum entropy, Entropy and EEG-fMRI, Epilepsy. The Inverse problem study combines topics in areas such as Ictal, Bayesian inference, Noise and Receiver operating characteristic. His study in the fields of Wavelet transform, Coiflet and Stationary wavelet transform under the domain of Wavelet overlaps with other disciplines such as Scaling.

His most cited work include:

  • Evaluation of EEG localization methods using realistic simulations of interictal spikes. (166 citations)
  • Evaluation of EEG localization methods using realistic simulations of interictal spikes. (166 citations)
  • Recording and analysis techniques for high-frequency oscillations (131 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Electroencephalography, Artificial intelligence, Pattern recognition, Neuroscience and Wavelet. Jean-Marc Lina works on Electroencephalography which deals in particular with Magnetoencephalography. His Artificial intelligence study incorporates themes from Inverse problem, Computer vision and Sensitivity.

The various areas that he examines in his Inverse problem study include Algorithm and Machine learning. His Pattern recognition course of study focuses on Epileptic discharge and Epileptic activity. In his research, Graphical model is intimately related to Entropy, which falls under the overarching field of Principle of maximum entropy.

He most often published in these fields:

  • Electroencephalography (59.04%)
  • Artificial intelligence (51.60%)
  • Pattern recognition (40.96%)

What were the highlights of his more recent work (between 2018-2021)?

  • Electroencephalography (59.04%)
  • Audiology (18.62%)
  • Sleep in non-human animals (11.17%)

In recent papers he was focusing on the following fields of study:

Jean-Marc Lina spends much of his time researching Electroencephalography, Audiology, Sleep in non-human animals, Artificial intelligence and Amplitude. His Electroencephalography study is focused on Neuroscience in general. His Audiology research includes themes of Non-rapid eye movement sleep, Cognition and Polysomnography.

His biological study spans a wide range of topics, including Tomography, Mr imaging and Pattern recognition. His work in Pattern recognition addresses issues such as Principle of maximum entropy, which are connected to fields such as Finger tapping, Algorithm and Weighting. His Magnetoencephalography research is multidisciplinary, incorporating perspectives in Ictal and Epilepsy.

Between 2018 and 2021, his most popular works were:

  • Sleep-Wake Cycle in Young and Older Mice. (16 citations)
  • Age-related cortical signatures of human sleep electroencephalography (12 citations)
  • Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors (11 citations)

In his most recent research, the most cited papers focused on:

  • Quantum mechanics
  • Artificial intelligence
  • Machine learning

His scientific interests lie mostly in Pattern recognition, Artificial intelligence, Ictal, Principle of maximum entropy and Neuroscience. The concepts of his Pattern recognition study are interwoven with issues in Supine position and Backpropagation. His Artificial intelligence study frequently draws connections to adjacent fields such as Cohen's kappa.

Jean-Marc Lina combines subjects such as Cortical map and Magnetoencephalography with his study of Ictal. Jean-Marc Lina has researched Principle of maximum entropy in several fields, including Tomography, Surgical planning and Statistical parametric mapping. His study focuses on the intersection of Neuroscience and fields such as Age related with connections in the field of Eye movement, Human brain and Electroencephalography.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Evaluation of EEG localization methods using realistic simulations of interictal spikes.

Christophe Grova;Jean Daunizeau;Jean Daunizeau;Jean-Marc Lina;Jean-Marc Lina;Christian G. Bénar.
NeuroImage (2006)

212 Citations

Complex Daubechies Wavelets

Jean-Marc Lina;Michel Mayrand.
Applied and Computational Harmonic Analysis (1995)

209 Citations

Recording and analysis techniques for high-frequency oscillations

G. A. Worrell;K. Jerbi;K. Kobayashi;Jean-Marc Lina.
Progress in Neurobiology (2012)

189 Citations

Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework

Jean Daunizeau;Christophe Grova;Guillaume Marrelec;Jérémie Mattout.
NeuroImage (2007)

158 Citations

Seizure prediction for therapeutic devices: A review.

Kais Gadhoumi;Kais Gadhoumi;Jean-Marc Lina;Florian Mormann;Jean Gotman;Jean Gotman.
Journal of Neuroscience Methods (2016)

155 Citations

WAVELET-BASED MULTIFRACTAL FORMALISM TO ASSIST IN DIAGNOSIS IN DIGITIZED MAMMOGRAMS

Pierre Kestener;Jean Marc Lina;Philippe Saint-Jean;Alain Arneodo.
Image Analysis & Stereology (2011)

123 Citations

Scalp EEG is not a Blur: It Can See High Frequency Oscillations Although Their Generators are Small

R. Zelmann;J. M. Lina;A. Schulze-Bonhage;J. Gotman.
Brain Topography (2014)

119 Citations

A short introduction to wavelets and their applications

C. Gargour;M. Gabrea;V. Ramachandran;J.-M. Lina.
IEEE Circuits and Systems Magazine (2009)

115 Citations

Oscillatory activity in parietal and dorsolateral prefrontal cortex during retention in visual short-term memory : additive effects of spatial attention and memory load

Stéphan Grimault;Nicolas Robitaille;Christophe Grova;Christophe Grova;Jean-Marc Lina;Jean-Marc Lina.
Human Brain Mapping (2009)

106 Citations

Concordance between distributed EEG source localization and simultaneous EEG-fMRI studies of epileptic spikes

Christophe Grova;Jean Daunizeau;Jean Daunizeau;Eliane Kobayashi;Andrew P. Bagshaw;Andrew P. Bagshaw.
NeuroImage (2008)

103 Citations

Best Scientists Citing Jean-Marc Lina

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Gregory A. Worrell

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Bin He

Bin He

Tongji University

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Benjamin H. Brinkmann

Benjamin H. Brinkmann

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Jean Gotman

Jean Gotman

Montreal Neurological Institute and Hospital

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Fabrice Bartolomei

Fabrice Bartolomei

Aix-Marseille University

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Matti Hämäläinen

Matti Hämäläinen

Harvard University

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Serge Vulliemoz

Serge Vulliemoz

University of Geneva

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Louis Lemieux

Louis Lemieux

University College London

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Fabrice Wendling

Fabrice Wendling

University of Rennes 1

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Christophe Grova

Christophe Grova

McGill University

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Karl J. Friston

Karl J. Friston

University College London

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Patrice Abry

Patrice Abry

École Normale Supérieure de Lyon

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Jean Daunizeau

Jean Daunizeau

Inserm : Institut national de la santé et de la recherche médicale

Publications: 15

Benjamin Audit

Benjamin Audit

École Normale Supérieure de Lyon

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Margitta Seeck

Margitta Seeck

Geneva College

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Julia Jacobs

Julia Jacobs

University of Freiburg

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Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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