Increasing the information transfer of EEG-based brain-computer interfaces

Objective

Abstract: A Brain-Computer-Interface (BCI) transforms brain activity into control signal. The aim is to improve the performance. In order to improve the performance, well investigate multivariate and non-linear parameters of the EEG. In order to get under control the curse of dimensionality, a twofold approach will be used. First, parametric autoregressive (AR) models including multivariate and non-linear extensions will be applied. AR parameters are known to be a so-called maximum entropy spectral estimator, which minimizes the number of parameters. In order words, the same number of parameters allows to describe the EEG in more detail. Second, support vector machines (SVM) will be applied, since SVM's are able to handle high-dimensional feature space.

Fields of science

• natural sciences › computer and information sciences › software
• engineering and technology › electrical engineering, electronic engineering, information engineering › electronic engineering › signal processing

Keywords

• Brain Computer interface
• Electroencephalogram
• biomedical signal processing
• machine learning

Programme(s)

• FP6-MOBILITY - Human resources and Mobility in the specific programme for research, technological development and demonstration "Structuring the European Research Area" under the Sixth Framework Programme 2002-2006

Topic(s)

• MOBILITY-2.1 - Marie Curie Intra-European Fellowships (EIF)

Call for proposal

FP6-2004-MOBILITY-5
See other projects for this call

Funding Scheme

EIF - Marie Curie actions-Intra-European Fellowships

Coordinator

FRAUNHOFER INSTITUT FIRST

Address

Hansastr. 27c
Munchen
Germany

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