What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Artificial intelligence
- Electroencephalography
The scientist’s investigation covers issues in Epilepsy, Electroencephalography, Neuroscience, Magnetic resonance imaging and Ictal.
Benjamin H. Brinkmann frequently studies issues relating to Nuclear medicine and Epilepsy.
The Nuclear medicine study combines topics in areas such as Temporal lobe and Ictal-Interictal SPECT Analysis by SPM.
His Electroencephalography study incorporates themes from Neurological disorder, Epileptogenesis, Brain mapping and Scalp.
His Magnetic resonance imaging research is multidisciplinary, incorporating elements of Image processing, Image restoration, Unsharp masking, Computer vision and Artificial intelligence.
His study in Ictal is interdisciplinary in nature, drawing from both Crowdsourcing and Audiology.
His most cited work include:
- Microseizures and the spatiotemporal scales of human partial epilepsy (223 citations)
- Effect of GLP-1 on gastric volume, emptying, maximum volume ingested, and postprandial symptoms in humans. (207 citations)
- Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction (204 citations)
What are the main themes of his work throughout his whole career to date?
Benjamin H. Brinkmann mainly focuses on Epilepsy, Electroencephalography, Ictal, Neuroscience and Artificial intelligence.
His Epilepsy study incorporates themes from Magnetic resonance imaging and Audiology.
His studies in Magnetic resonance imaging integrate themes in fields like Single-photon emission computed tomography and Computer vision.
His study looks at the relationship between Electroencephalography and topics such as Scalp, which overlap with Biomedical engineering.
His work investigates the relationship between Ictal and topics such as Nuclear medicine that intersect with problems in Ictal-Interictal SPECT Analysis by SPM and Image processing.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition.
He most often published in these fields:
- Epilepsy (61.59%)
- Electroencephalography (35.51%)
- Ictal (29.71%)
What were the highlights of his more recent work (between 2018-2021)?
- Epilepsy (61.59%)
- Electroencephalography (35.51%)
- Ictal (29.71%)
In recent papers he was focusing on the following fields of study:
His main research concerns Epilepsy, Electroencephalography, Ictal, Neuroscience and Audiology.
His Epilepsy research integrates issues from Retrospective cohort study, Ambulatory, Scalp, Pediatrics and Cohort.
His work in the fields of Intracranial eeg overlaps with other areas such as Clinical neurology.
His research in Ictal intersects with topics in High density eeg, Magnetic resonance imaging and Nuclear medicine.
His Audiology research incorporates elements of Mobile apps, Seizure onset zone, Seizure onset and Seizure diary.
Benjamin H. Brinkmann works mostly in the field of Ictal-Interictal SPECT Analysis by SPM, limiting it down to concerns involving Statistical parametric mapping and, occasionally, Anesthesia.
Between 2018 and 2021, his most popular works were:
- Intracerebral EEG Artifact Identification Using Convolutional Neural Networks (22 citations)
- Forecasting cycles of seizure likelihood. (21 citations)
- Laser ablation for mesial temporal epilepsy: a multi-site, single institutional series. (21 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Artificial intelligence
- Electroencephalography
Benjamin H. Brinkmann focuses on Electroencephalography, Epilepsy, Artificial intelligence, Pattern recognition and Convolutional neural network.
The Electroencephalography study combines topics in areas such as Real-time computing, Leverage, Audiology and Linear classifier.
His Epilepsy study deals with the bigger picture of Neuroscience.
While the research belongs to areas of Neuroscience, he spends his time largely on the problem of Potential biomarkers, intersecting his research to questions surrounding Temporal lobe.
His Pattern recognition study combines topics from a wide range of disciplines, such as University hospital, Electrophysiology and Identification.
His study looks at the relationship between Convolutional neural network and fields such as Deep learning, as well as how they intersect with chemical problems.
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