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This thesis explores latent-variable probabilistic models for the analysis and classification of electroenchephalographic (EEG) signals used in Brain Computer Interface (BCI) systems. The first part of the thesis focuses on the use of probabilistic methods ...
We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate B ...
This thesis explores latent-variable probabilistic models for the analysis and classification of electroenchephalographic (EEG) signals used in Brain Computer Interface (BCI) systems. The first part of the thesis focuses on the use of probabilistic methods ...