Mental tasks selection method for a SVM-based BCI system

In this work, a study that analyzes the best combinations of mental tasks in a Brain-Computer Interface (BCI) using a classifier based on Support Vector Machine (SVM) is presented. To that end, twelve mental tasks of different nature are analyzed and the results of the classification for the combinations of two, three and four tasks are obtained. Four volunteers performed registers of the twelve tasks. The main goal is to find the combination of more than three mental tasks that obtains the higher reliability to apply it in future complex applications that require the use of more than three mental control commands. After a selection procedure, the results obtained show higher success percentages and important differences according to the nature of the mental tasks, which suggest that it is possible to differentiate with enough reliability between more than three mental tasks using the methodology proposed.