All brain-computer interface (BCI) groups that have published results of studies 
involving a large number of users performing BCI control based on the voluntary 
modulation of sensorimotor rhythms (SMR) report that BCI control could not be 
achieved by a non-negligible number of subjects (estimated 20% to 25%). This 
failure of the BCI system to read the intention of the user is one of the 
greatest problems and challenges in BCI research. There are two main causes for 
this problem in SMR-based BCI systems: either no idle SMR is observed over motor 
areas of the user, or this idle rhythm is not modulated during motor imagery, 
resulting in a classification performance lower than 70% (criterion level) that 
renders the control of a BCI application (like a speller) difficult or 
impossible. Previously, we introduced the concept of machine learning based 
co-adaptive calibration, which provided substantially improved performance for a 
variety of users. Here, we use a similar approach and investigate to what extent 
co-adaptive learning enables significant BCI control for completely novice 
users, as well as for those who could not achieve control with a conventional 
SMR-based BCI.