An important factor in custom designing a brain computer interface, BCI, is the estimation of the values of its parameters. This paper proposes a fully automatic algorithm that uses Bayesian optimization to tune the hyper-parameters of a synchronous BCI. The algorithm finds a large number of possible sets of values for the hyper-parameters. Each set is then used to train the classifier and the results over the possible sets of hyper-parameter values are aggregated. In this paper we consider a simple motor imagery based BCI with two parameters: the EEG frequency bands and the time intervals from which the features are extracted. We use the linear discriminant analysis classifier and aggregate all results using multi-response linear regression. Experiments using the BCI competition III dataset 3b show that our proposed method results in considerable improvement in the accuracy of a BCI. The average accuracy of our method was 2.6% better than the best results obtained by existing methods.