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.
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