Towards a cure for BCI illiteracy: Machine-learning based co-adaptive learning
Brain-Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as acquired, e.g., by EEG. One of the biggest challenges in BCI research is to understand and solve the problem of ‘BCI Illiteracy’, which is that BCI control does not work for a non-negligible portion of subjects (estimated 15% to 30%). In a screening study, N=80 subjects performed motor imagery first in a calibration (i.e. without feedback) measurement and then in a feedback measurement in which they could control a 1D cursor application. Coarsely, we observed three categories of subjects: subjects for whom (I) a classifier could be successfully trained and who performed feedback with good accuracy; (II) a classifier could be successfully trained, but feedback did not work well; (III) no classifier with acceptable accuracy could be trained. While subjects of Cat. II had obviously difficulties with the transition from offline on online operation, subjects of Cat. III did not show the expected modulation of sensorimotor rhythms (SMRs): either no SMR idle rhythm was observed over motor areas, or this idle rhythm was not attenuated during motor imagery.