Subject-independent, SSVEP-based BCI: Trading off among accuracy, responsiveness and complexity

Brain-Computer Interface (BCI) can provide users with an alternative/augmentative interaction path, based on the interpretation of their brain activity. Steady State Visual Evoked Potential (SSVEP) is a good candidate for BCI-enabled communication/control applications. In this paper, we compare different reference signal processing methods, including two we developed ad hoc, assessing how they perform with respect to different indicators (not necessarily convergent, such as accuracy, computational effort and responsiveness). All the tests are performed on the subject population as a whole, in an effort to produce subject-independent methods. We also discuss a strategy for improving the classification accuracy by introducing an indicator related to the prediction confidence. Finally, a method for adaptively changing the length of the observed EEG window is presented.