What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Electroencephalography
Maarten De Vos focuses on Electroencephalography, Brain–computer interface, Speech recognition, Artificial intelligence and Brain activity and meditation.
Maarten De Vos is interested in Auditory oddball, which is a field of Electroencephalography.
His Brain–computer interface study combines topics in areas such as Event-related potential, Eeg monitoring, Natural interaction, Psychophysiology and Robustness.
Maarten De Vos has included themes like Eeg electrodes, Human–computer interaction, Ecological validity and Pattern recognition in his Artificial intelligence study.
His biological study spans a wide range of topics, including Cognitive psychology, Cognition and Simulation.
Maarten De Vos has researched Communication in several fields, including Field, Linear discriminant analysis, Wireless eeg and Auditory cortex.
His most cited work include:
- Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal (972 citations)
- How about taking a low-cost, small, and wireless EEG for a walk? (344 citations)
- How about taking a low-cost, small, and wireless EEG for a walk? (344 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Electroencephalography, Artificial intelligence, Pattern recognition, Speech recognition and Audiology.
His study explores the link between Electroencephalography and topics such as Independent component analysis that cross with problems in Artifact.
His studies deal with areas such as Sleep staging, Machine learning and Computer vision as well as Artificial intelligence.
His Pattern recognition study integrates concerns from other disciplines, such as Recurrent neural network and Preprocessor.
His work on Cochlear implant as part of his general Audiology study is frequently connected to Postmenstrual Age, thereby bridging the divide between different branches of science.
The concepts of his Brain–computer interface study are interwoven with issues in Auditory oddball, Brain activity and meditation and Communication.
He most often published in these fields:
- Electroencephalography (55.36%)
- Artificial intelligence (39.29%)
- Pattern recognition (23.66%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (39.29%)
- Electroencephalography (55.36%)
- Pattern recognition (23.66%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Electroencephalography, Pattern recognition, Machine learning and Convolutional neural network.
His Artificial intelligence research is multidisciplinary, relying on both Sleep staging and Neonatal seizure.
His Electroencephalography research integrates issues from Random forest, Speech recognition and Sleep Stages, Polysomnography.
His Pattern recognition research is multidisciplinary, incorporating elements of Recurrent neural network, Filter bank and Constant false alarm rate.
He interconnects Data modeling and Sequential model in the investigation of issues within Machine learning.
Maarten De Vos usually deals with Convolutional neural network and limits it to topics linked to Feature selection and Gait.
Between 2017 and 2021, his most popular works were:
- Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal (972 citations)
- Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification (82 citations)
- SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging (73 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Maarten De Vos spends much of his time researching Artificial intelligence, Electroencephalography, Convolutional neural network, Pattern recognition and Sleep Stages.
As a part of the same scientific study, he usually deals with the Artificial intelligence, concentrating on Machine learning and frequently concerns with Sleep staging.
His Electroencephalography study combines topics from a wide range of disciplines, such as Feature data, Speech recognition, Random forest and Polysomnography.
The various areas that he examines in his Convolutional neural network study include Artificial neural network, Feature selection, Audiology and Brain development.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Recurrent neural network, Filter bank, Constant false alarm rate and Neonatal seizure.
His studies in Sleep Stages integrate themes in fields like Quiet sleep, Electrooculography and Brain maturation.
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