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Reducing calibration time for brain-computer interfaces: A clustering approach

: Krauledat, M.; Schröder, M.; Blankertz, B.; Müller, K.-R.

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Schölkopf, B.:
Advances in Neural Information Processing Systems 19 : Proceedings of the 20th Conference on Advances in Neural Information Processing Systems (NIPS), which took place in Vancouver, British Columbia, Canada, on December 4 - 7, 2006
Cambridge, MA: MIT Press, 2007 (A Bradford book)
ISBN: 0-262-19568-2
ISBN: 978-0-262-19568-3
Annual Conference on Neural Information Processing Systems (NIPS) <20, 2006, Vancouver>
Conference Paper, Electronic Publication
Fraunhofer FIRST ()

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 concatenationmethods. 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.