Minimizing Calibration Time for Brain Reading

Abstract

Machine learning is increasingly used to autonomously adapt brain-machine interfaces to user-specific brain patterns. In order to minimize the preparation time of the system, it is highly desirable to reduce the length of the calibration procedure, during which training data is acquired from the user, to a minimum. One recently proposed approach is to reuse models that have been trained in historic usage sessions of the same or other users by utilizing an ensemble-based approach. In this work, we propose two extensions of this approach which are based on the idea to combine predictions made by the historic ensemble with session-specific predictions that become available once a small amount of training data has been collected. These extensions are particularly useful for Brain Reading Interfaces (BRIs), a specific kind of brain-machine interfaces. BRIs do not require that user feedback is given and thus, additional training data may be acquired concurrently to the usage session. Accordingly, BRIs should initially perform well when only a small amount of training data acquired in a short calibration procedure is available and allow an increased performance when more training data becomes available during the usage session. An empirical offline-study in a testbed for the use of BRIs to support robotic telemanipulation shows that the proposed extensions allow to achieve this kind of behavior.