There is a growing interest in the use of physiological signals for communication and operation of devices for the severely motor disabled as well as for healthy people. A few groups around the world have developed brain-computer interfaces (BCIs) that rely upon the recognition of motor-related tasks (i.e.,imagination of movements) from on-line EEG signals. In this paper we seek to find and analyze the set of relevant EEG features that best differentiate spontaneous motorrelated mental tasks from each other. This study empirically demonstrates the benefits of heuristic feature selection methods for EEG-based classification of mental tasks. In particular,it is shown that the classifier performance improves for all the considered subjects with only a small proportion of features. Thus,the use of just those relevant features increases the efficiency of the brain interfaces and,most importantly,enables a greater level of adaptation of the personal BCI to the individual user.