Raw and Artificially Generated Data to Improve the Development of Brain-Computer Interfaces: Proof of Concept

Eduardo Carabez, Takanori Sato, Isao Nambu

Abstract: Performance of brain computer interfaces (BCI) highly depends on the how well their preprocessing and classification modules are implemented. Preprocessing allows to get cleaner signals and makes their classification easier. However, we hypothesize that information loss occurring due to the preprocessing might prevent the classifier from performing better instead of helping it. To test our hypothesis, we implemented a classifier using raw data from the 2nd Wadsworth BCI Dataset from BCI competition III and tested it with that same dataset as well as the Wadsworth BCI Dataset from BCI competition II. Furthermore, the impact in the classifier performance when augmenting the classifier training set with artificially generated data was assessed. We found that although the classifier performed similarly in most cases, the case with raw data and an augmented training dataset performed round 3 % better than the others in the same conditions on a 5-fold cross validation exercise, suggesting possible positive effects of raw and artificially generated data.