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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
Pages753-760
Number of pages8
Publication statusPublished - 2007
Event20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada
Duration: 2006 Dec 42006 Dec 7

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other20th Annual Conference on Neural Information Processing Systems, NIPS 2006
CountryCanada
CityVancouver, BC
Period06/12/406/12/7

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • Cite this

    Krauledat, M., Schröder, M., Blankertz, B., & Müller, K. R. (2007). Reducing calibration time for brain-computer interfaces: A clustering approach. In Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference (pp. 753-760). (Advances in Neural Information Processing Systems).