Selection of the best mental tasks 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, 12 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 12 tasks. The main goal is to find the combination of more than three mental tasks that obtains the highest reliability to apply it in future complex applications that require the use of more than three control commands. After a selection procedure, the results obtained show higher success rates. Using the information provided by every single electrode, an average of 87.10% is obtained as success rate for the classification of two mental tasks, 65.67% for three mental tasks and 50.76% for four mental tasks. Moreover combinations of the best electrodes are studied, improving the accuracy of the system. Using the best five electrodes, averages of 91.42%, 72.89% and 59.75% are obtained classifying two, three and four mental tasks respectively. These results suggest that it is possible to differentiate with enough reliability between more than three mental tasks using the methodology proposed.