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.
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