Towards Efficient Auditory BCI Through Optimized Paradigms and Methods
To date, the brain-computer interface (BCI) based on visual stimulation is by far the most investigated. This makes a lot of sense given the excellent visual capabilities of humans, and it has been a most successful approach. Nevertheless, a non-negligible part of the BCI end-user population with advanced paralysis is incapable of directing their eye gaze or of seeing at all. Traditional visual BCIs will rarely be a solution for these end-users and alternative paradigms are required that rely on covert attention. Furthermore, though BCIs already benefit greatly from statistical methods coming from the field of machine learning, a faster and robust performance is required for adoption by the clinical community. This thesis contributes in two key facets in an attempt to take a step towards full inclusion of all end-users.
