What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Magnetic resonance imaging
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image processing.
His Artificial intelligence research is multidisciplinary, relying on both Computer graphics and Mr images.
His study in Computer vision is interdisciplinary in nature, drawing from both Algorithm and Magnetic resonance imaging.
His Pattern recognition study combines topics from a wide range of disciplines, such as Neurophysiology, Imaging phantom, Voxel and Sulcus.
His Image segmentation and Multiple sclerosis lesion study in the realm of Segmentation connects with subjects such as Test data and Multigrid method.
The various areas that he examines in his Non-local means study include Fractional anisotropy and Noise reduction.
His most cited work include:
- An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images (828 citations)
- Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques (827 citations)
- Nonlocal Means-Based Speckle Filtering for Ultrasound Images (412 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Magnetic resonance imaging and Segmentation.
His Artificial intelligence study combines topics in areas such as Imaging phantom and Algorithm.
His work on Image registration, Noise reduction, Non-local means and Ground truth is typically connected to Transformation as part of general Computer vision study, connecting several disciplines of science.
His research is interdisciplinary, bridging the disciplines of Voxel and Pattern recognition.
In his research, Partial volume is intimately related to Perfusion, which falls under the overarching field of Magnetic resonance imaging.
His Segmentation study focuses mostly on Scale-space segmentation, Level set and Cut.
He most often published in these fields:
- Artificial intelligence (49.11%)
- Computer vision (30.60%)
- Pattern recognition (22.42%)
What were the highlights of his more recent work (between 2018-2021)?
- Neurofeedback (10.68%)
- EEG-fMRI (6.41%)
- Electroencephalography (11.03%)
In recent papers he was focusing on the following fields of study:
Christian Barillot mostly deals with Neurofeedback, EEG-fMRI, Electroencephalography, Motor imagery and Artificial intelligence.
The study incorporates disciplines such as Rehabilitation, Physical medicine and rehabilitation, Stroke, Brain–computer interface and Neuroimaging in addition to Neurofeedback.
His biological study spans a wide range of topics, including Redundancy, Measure, Computer vision and Pattern recognition.
His research in Computer vision is mostly concerned with Image quality.
Particularly relevant to Segmentation is his body of work in Pattern recognition.
His Magnetic resonance imaging study incorporates themes from Noise reduction and Perfusion.
Between 2018 and 2021, his most popular works were:
- A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients (36 citations)
- White matter abnormalities in depression: A categorical and phenotypic diffusion MRI study (15 citations)
- Unsupervised Domain Adaptation With Optimal Transport in Multi-Site Segmentation of Multiple Sclerosis Lesions From MRI Data. (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Magnetic resonance imaging
- Statistics
Christian Barillot mainly focuses on Neurofeedback, Motor imagery, Electroencephalography, Brain activity and meditation and EEG-fMRI.
The Neurofeedback study combines topics in areas such as Audiology, Haptic technology, Artificial intelligence and Stroke recovery.
His research in Electroencephalography intersects with topics in Healthy volunteers, Multi modal data, Pattern recognition, Functional magnetic resonance imaging and Neuroimaging.
The concepts of his Functional magnetic resonance imaging study are interwoven with issues in Parietal lobe, Modality and Speech recognition.
His Brain activity and meditation research is multidisciplinary, incorporating perspectives in Interface, Brain–computer interface, Human–computer interaction, Visual perception and Stimulus modality.
His EEG-fMRI research incorporates elements of Physical medicine and rehabilitation, Corticospinal tract, Stroke and Motor cortex, Primary motor cortex.
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.
