What is he best known for?
The fields of study he is best known for:
- Neuroscience
- Statistics
- Artificial intelligence
Arjan Hillebrand mainly focuses on Magnetoencephalography, Neuroscience, Artificial intelligence, Visual cortex and Algorithm.
A large part of his Magnetoencephalography studies is devoted to Synthetic-aperture magnetometry.
His Neuroscience research is multidisciplinary, incorporating elements of Topology, Minimum spanning tree and Power graph analysis.
Arjan Hillebrand has researched Artificial intelligence in several fields, including Machine learning, Computer vision and Pattern recognition.
Arjan Hillebrand works mostly in the field of Algorithm, limiting it down to topics relating to Image and, in certain cases, Beamforming, Set and Adaptive beamformer, as a part of the same area of interest.
His research in Neuroimaging intersects with topics in Cognition and Human brain.
His most cited work include:
- Good practice for conducting and reporting MEG research (429 citations)
- A new approach to neuroimaging with magnetoencephalography. (372 citations)
- A quantitative assessment of the sensitivity of whole-head MEG to activity in the adult human cortex. (309 citations)
What are the main themes of his work throughout his whole career to date?
Arjan Hillebrand mainly investigates Magnetoencephalography, Neuroscience, Artificial intelligence, Electroencephalography and Resting state fMRI.
His specific area of interest is Magnetoencephalography, where he studies Synthetic-aperture magnetometry.
His research integrates issues of Multiple sclerosis, Parkinson's disease and Cognitive decline in his study of Neuroscience.
His Multiple sclerosis research is multidisciplinary, incorporating perspectives in Magnetic resonance imaging and Atrophy.
His research on Artificial intelligence also deals with topics like
- Pattern recognition together with Communication,
- Algorithm which connect with Image resolution.
Arjan Hillebrand has included themes like Stimulus and Premovement neuronal activity in his Visual cortex study.
He most often published in these fields:
- Magnetoencephalography (68.25%)
- Neuroscience (44.44%)
- Artificial intelligence (19.05%)
What were the highlights of his more recent work (between 2018-2021)?
- Magnetoencephalography (68.25%)
- Neuroscience (44.44%)
- Resting state fMRI (15.87%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Magnetoencephalography, Neuroscience, Resting state fMRI, Epilepsy and Parkinson's disease.
His Magnetoencephalography research is classified as research in Electroencephalography.
His Neuroscience research incorporates elements of Multiple sclerosis and Dementia, Cognitive decline.
His Resting state fMRI study combines topics from a wide range of disciplines, such as Noise, Coherence, Premovement neuronal activity and Autoregressive model.
The study incorporates disciplines such as Cognitive psychology, Peri, Centrality and Tumor region in addition to Epilepsy.
Neuroimaging is closely connected to Migraine in his research, which is encompassed under the umbrella topic of Audiology.
Between 2018 and 2021, his most popular works were:
- Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity. (20 citations)
- Mapping functional brain networks from the structural connectome : Relating the series expansion and eigenmode approaches (14 citations)
- The road ahead in clinical network neuroscience (13 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Neuroscience
- Internal medicine
Arjan Hillebrand spends much of his time researching Magnetoencephalography, Neuroscience, Electroencephalography, Clinical neurology and Functional magnetic resonance imaging.
The various areas that Arjan Hillebrand examines in his Magnetoencephalography study include Dynamic functional connectivity, Cognition, Spectral density, Beta band and Epilepsy.
The concepts of his Dynamic functional connectivity study are interwoven with issues in Artificial intelligence and Power graph analysis.
His study connects Cognitive decline and Neuroscience.
His work on Epilepsy surgery as part of general Electroencephalography research is frequently linked to Correlation, bridging the gap between disciplines.
His biological study spans a wide range of topics, including Adjacency matrix, Statistical physics and Laplacian matrix.
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