Discriminating between Color Exposure and Idle State using EEG Signals for BCI Application

Brain-Computer Interfaces are systems that would allow either the reintegration of handicapped people or effective control of devices by thoughts for healthy people. Several neuroparadigms (motor imagery, SSVEPs, P300, SCP among others) have been explored to control a BCI, but passive color exposure is one of the most novels. However, the analysis of the potential of this task for being used in a self-paced BCI has not been studied enough. In this paper, the first step towards a self-paced color-exposure-based BCI is presented. This is done by the analysis of the differences between the interest activities (RGB colors) and idle state. Specifically, we extract EMD (Empirical Mode Decomposition)-based features from the EEG signals and then these are classified using 4 classifiers (SVM, random forest, Naive Bayes and KNN). We then present an analysis of the performances for these two classes (RGB colors vs idle state) obtained for seven subjects, highlighting SVM's average performance (93.27% for all of them). Also, the differences between the two classes (color exposure and idle state) suggest that they do not depend on a specific subject. Last, we provide experimental evidence that there is no a specific color that could be distinguished better regardless idle state, reaching average accuracies of 85.01%, 85.56%, and 86.91% for red, green and blue vs idle state, respectively.