Toward estimation of brain connectivity as new feature for BCI application
Introduction Modern neuroimaging has provided unequivocal evidence that brain functions are
subserved by multiple areas functionally interconnected. Brain computer interfaces (BCIs) may benefit
from feature extraction based on metrics derived from brain connectivity [1]-[3]. However, reaching the
minimally required accuracy when few data samples are available as in single trial or real-time
connectivity applications is still challenging. Variables selection algorithms could represent a valuable