On transferring spatial filters in a brain reading scenario

Machine learning approaches are increasingly used in brain-machine-interfaces to allow automatic adaptation to user-specific brain patterns. One of the most crucial factors for the practical success of these systems is that this adaptation can be achieved with a minimum amount of training data since training data needs to be recorded during a calibration procedure prior to the actual usage session. To this end, one promising approach is to reuse models based on data recorded in preceding sessions of the same or of other users. In this paper, we investigate under which conditions it is favorable to reuse models (more specifically spatial filters) trained on data from historic sessions compared to learning new spatial filters on the current session's calibration data. We present an empirical study in a scenario in which Brain Reading, a particular kind of brain-machine-interface, is used to support robotic telemanipulation.