What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Brain–computer interface, Electroencephalography, Artificial intelligence, Speech recognition and Interface.
His Brain–computer interface research incorporates themes from Oddball paradigm, Brain activity and meditation, N2pc and Human–computer interaction.
His Electroencephalography research incorporates elements of Linear discriminant analysis, Multivariate statistics, Audiology and Neuroprosthetics.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Neuroimaging, Interfacing and Pattern recognition.
The various areas that he examines in his Speech recognition study include Sensorimotor rhythm, Neurophysiology, Information transfer, Calibration and Boosting.
His studies deal with areas such as Preprocessor, Signal processing, Adaptation and Output device as well as Interface.
His most cited work include:
- Optimizing Spatial filters for Robust EEG Single-Trial Analysis (1350 citations)
- Single-Trial Analysis and Classification of ERP Components - a Tutorial (747 citations)
- On the interpretation of weight vectors of linear models in multivariate neuroimaging. (703 citations)
What are the main themes of his work throughout his whole career to date?
Benjamin Blankertz focuses on Brain–computer interface, Artificial intelligence, Electroencephalography, Speech recognition and Interface.
The concepts of his Brain–computer interface study are interwoven with issues in Stimulus, Brain activity and meditation, Interfacing and Human–computer interaction.
Benjamin Blankertz has included themes like Covert, Machine learning, Computer vision and Pattern recognition in his Artificial intelligence study.
His Electroencephalography research includes elements of Neurophysiology, Cognition, Decoding methods, Neuroimaging and Simulation.
His work in Speech recognition addresses subjects such as Eye movement, which are connected to disciplines such as Visual perception.
His research integrates issues of User interface, Adaptation, Proof of concept, Data set and Pattern recognition in his study of Interface.
He most often published in these fields:
- Brain–computer interface (67.09%)
- Artificial intelligence (42.31%)
- Electroencephalography (38.46%)
What were the highlights of his more recent work (between 2014-2021)?
- Brain–computer interface (67.09%)
- Artificial intelligence (42.31%)
- Electroencephalography (38.46%)
In recent papers he was focusing on the following fields of study:
Benjamin Blankertz mainly investigates Brain–computer interface, Artificial intelligence, Electroencephalography, Speech recognition and Pattern recognition.
His Brain–computer interface research is classified as research in Interface.
His Artificial intelligence study combines topics in areas such as Software, Decoding methods, Computer vision, Machine learning and Interfacing.
His work carried out in the field of Electroencephalography brings together such families of science as Stimulus, Image quality, Visual evoked potentials and Perception.
He has researched Speech recognition in several fields, including Workload, Neurophysiology and Simulation.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Regularization, Covariance matrix, Multivariate statistics and Signal processing.
Between 2014 and 2021, his most popular works were:
- BNCI Horizon 2020: Towards a Roadmap for the BCI Community (123 citations)
- The Berlin Brain-Computer Interface: Progress Beyond Communication and Control. (122 citations)
- A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes (101 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Artificial intelligence, Electroencephalography, Brain–computer interface, Speech recognition and Pattern recognition.
His Artificial intelligence research integrates issues from Interfacing, Computer vision and Eeg oscillations.
His studies in Electroencephalography integrate themes in fields like Stimulus, Optimization problem, Visual evoked potentials and Riemannian geometry.
He combines subjects such as Cognitive psychology and Movement with his study of Brain–computer interface.
His work deals with themes such as Biometrics, Login, Simulation, Authentication and Auditory perception, which intersect with Speech recognition.
His Pattern recognition research includes themes of Context, Weighting, Manifold, Covariance matrix and Signal processing.
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