T
Thilo Hinterberger
Researcher at University of Tübingen
Publications - 93
Citations - 10541
Thilo Hinterberger is an academic researcher from University of Tübingen. The author has contributed to research in topics: Brain–computer interface & Electroencephalography. The author has an hindex of 40, co-authored 77 publications receiving 9761 citations. Previous affiliations of Thilo Hinterberger include University Medical Center Freiburg.
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BCI2000: a general-purpose brain-computer interface (BCI) system
TL;DR: This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system.
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A spelling device for the paralysed
Niels Birbaumer,N. Ghanayim,Thilo Hinterberger,Iver H. Iversen,Boris Kotchoubey,Andrea Kübler,J. Perelmouter,Edward Taub,Herta Flor +8 more
TL;DR: A new means of communication for the completely paralysed that uses slow cortical potentials of the electro-encephalogram to drive an electronic spelling device is developed.
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The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials
Benjamin Blankertz,Klaus-Robert Müller,Gabriel Curio,Theresa M. Vaughan,Gerwin Schalk,Jonathan R. Wolpaw,Alois Schlögl,C. Neuper,Gert Pfurtscheller,Thilo Hinterberger,Michael Schröder,Niels Birbaumer +11 more
TL;DR: The BCI Competition 2003 was organized to evaluate the current state of the art of signal processing and classification methods and the results and function of the most successful algorithms were described.
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The thought translation device (TTD) for completely paralyzed patients
Niels Birbaumer,Andrea Kübler,N. Ghanayim,Thilo Hinterberger,J. Perelmouter,Jochen Kaiser,Iver H. Iversen,Boris Kotchoubey,Nicola Neumann,Herta Flor +9 more
TL;DR: The thought translation device trains locked-in patients to self-regulate slow cortical potentials of their electroencephalogram (EEG) to demonstrate the usefulness of the thoughttranslation device as an alternative communication channel in motivated totally paralyzed patients with amyotrophic lateral sclerosis.
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Support vector channel selection in BCI
TN Lal,Michael Schröder,Thilo Hinterberger,Jason Weston,Martin Bogdan,Niels Birbaumer,Bernhard Schölkopf +6 more
TL;DR: Recursive Feature Elimination and Zero-Norm Optimization which are based on the training of support vector machines (SVM) can provide more accurate solutions than standard filter methods for feature selection for EEG channels.