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.
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.
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.
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.
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.
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
B. Blankertz;R. Tomioka;S. Lemm;M. Kawanabe.
IEEE Signal Processing Magazine (2008)
Single-Trial Analysis and Classification of ERP Components - a Tutorial
Benjamin Blankertz;Steven Lemm;Matthias Sebastian Treder;Stefan Haufe.
The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.
Benjamin Blankertz;Guido Dornhege;Matthias Krauledat;Klaus Robert Müller.
The BCI competition III: validating alternative approaches to actual BCI problems
B. Blankertz;K.-R. Muller;D.J. Krusienski;G. Schalk.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2006)
The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials
B. Blankertz;K.-R. Muller;G. Curio;T.M. Vaughan.
IEEE Transactions on Biomedical Engineering (2004)
On the interpretation of weight vectors of linear models in multivariate neuroimaging.
Stefan Haufe;Frank C. Meinecke;Kai Görgen;Sven Dähne.
Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms
G. Dornhege;B. Blankertz;G. Curio;K.-R. Muller;K.-R. Muller.
IEEE Transactions on Biomedical Engineering (2004)
Classifying Single Trial EEG: Towards Brain Computer Interfacing
Benjamin Blankertz;Gabriel Curio;Klaus-Robert Müller.
neural information processing systems (2001)
Spatio-spectral filters for improving the classification of single trial EEG
S. Lemm;B. Blankertz;G. Curio;K.-R. Muller.
IEEE Transactions on Biomedical Engineering (2005)
Introduction to machine learning for brain imaging.
Steven Lemm;Benjamin Blankertz;Thorsten Dickhaus;Klaus Robert Müller.
Profile was last updated on December 6th, 2021.
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