Common Spatial Patterns (CSP) is a feature extraction method suited for two-class problems. However, there are some alternatives to apply it for multiclass tasks by using a group of ensemble classifiers that divide the problem into different binary classification tasks, from which the final decision is inferred as the combination of their responses. Nevertheless, there is another approach to extend CSP for multiple classes to a one-step-method by approximating the joint diagonalization of their covariance matrices. In this study, in order to identify whether CSP by Joint Approximate Diagonalization (JAD) represents an outperforming alternative to the standard mulMclass CSP alternatives, four different methods were applied on dataset 2a used in BCI competition IV.