Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Matthias Krauledat, Michael Schröder, Benjamin Blankertz, Klaus-Robert Müller


Up to now even subjects that are experts in the use of machine learning based BCI systems still have to undergo a calibration session of about 20-30 min. From this data their (movement) intentions are so far infered. We now propose a new paradigm that allows to completely omit such calibration and instead transfer knowledge from prior sessions. To achieve this goal we first define normalized CSP features and distances in-between. Second, we derive prototypical features across sessions: (a) by clustering or (b) by feature concatenation methods. Finally, we construct a classifier based on these individualized prototypes and show that, indeed, classifiers can be successfully transferred to a new session for a number of subjects.