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E2: Decoding of movement-related brain signals: Improvements by brain and machine learning

E2: Decoding of movement-related brain signals: Improvements by brain and machine learning

 

bmbf_logo_en.gifChristoph BraunS, Niels BirbaumerS, Carsten MehringJ, Ad AertsenA


S = Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen
J = Animal Physiol. & Neurobiol, Inst. of Biol.I
A = Neurobiology and Biophysics;

About the project

In the "Bernstein" collaboration "Decoding of movement-related brain signals: Improvements by brain and machine learning" between the universities of Freiburg and Tübingen it is intended to use theoretical and experimental know-how in a synergistic way to promote the understanding, application and optimization of on-line analysis of brain signals, required for the development of brain-computer-interfaces. In particular, patterns of cortical motor activity that are associated with specific movements should be reliably detected. The reliability of the categorization of brain signals will be improved by training subjects to generate specific patterns of brain activity (brain learning) and by using adaptive classification algorithms analyzing the recorded signals (machine learning). It is planned to focus on a well defined classification problem in order to systematically investigate and improve approaches suitable for the discrimination of specific brain activities. Therefore, brain activity will be studied in a so called center-out task, in which subjects move their hand outwards in different directions starting from a marked point.

 

Funded by the BMBF

 

Project closed

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