Brain-Machine Interfaces can help users participate in routine tasks, such as moving around. The scientific community works daily in an attempt to offer increasingly robust Brain-Machine Interface systems, with better responses to user commands. However, these works usually focus on improving the system itself. Therefore, the objective of this work is to offer an alternative to the users to help in the learning of the use of equipment of a Brain-Machine Interface based on the imagination of movements. For this, a computational tool based on a virtual game is developed in an attempt to improve the accuracy of users in controlling the devices of these
systems. The results show that the tool works when connected to a Brain-Machine Interface, and can serve as an alternative in the process of collecting EEG signals. Throughout this work, programming languages dedicated to ICMs, such as OpenVibe, are used, as well as a language widely used in the programming of electronic games, Python. In the experiment carried out with 8 volunteers, there is no discrepant difference between the classification rates performed with the aid of the conventional protocol and the ICM Space Game, approximately 56% for both
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