  A brain-computer interface (BCI) is a system that aims for establishing a
non-muscular communication path for subjects who had suffer from a
neurodegenerative disease. Many BCI systems make use of the phenomena of
event-related synchronization and de-synchronization of brain waves as a main
feature for classification of different cognitive tasks. However, the temporal
dynamics of the electroencephalographic (EEG) signals contain additional
information that can be incorporated into the inference engine in order to
improve the performance of the BCIs. This information about the dynamics of the
signals have been exploited previously in BCIs by means of generative and
discriminative methods. In particular, hidden Markov models (HMMs) have been
used in previous works. These methods have the disadvantage that the model
parameters such as the number of hidden states and the number of Gaussian
mixtures need to be fix "a priori". In this work, we propose a Bayesian
nonparametric model for brain signal classification that does not require "a
priori" selection of the number of hidden states and the number of Gaussian
mixtures of a HMM. The results show that the proposed model outperform other
methods based on HMM as well as the winner algorithm of the BCI competition IV.
