Improving speed and accuracy of brain-computer interfaces using readiness potential features

M. Krauledat, G. Dornhege, B. Blankertz, F. Losch, G. Curio, K. R. Müller

Research output: Contribution to journalConference article

30 Citations (Scopus)

Abstract

To enhance human interaction with machines, research interest is growing to develop a 'Brain-Computer Interface', which allows communication of a human with a machine only by use of brain signals. So far, the applicability of such an interface is strongly limited by low bit-transfer rates, slow response times and long training sessions for the subject. The Berlin Brain-Computer Interface (BBCI) project is guided by the idea to train a computer by advanced machine learning techniques both to improve classification performance and to reduce the need of subject training. In this paper we present two directions in which Brain-Computer Interfacing can be enhanced by exploiting the lateralized readiness potential: (1) for establishing a rapid response BCI system that can predict the laterality of upcoming finger movements before EMG onset even in time critical contexts, and (2) to improve information transfer rates in the common BCI approach relying on imagined limb movements.

Original languageEnglish
Pages (from-to)4511-4515
Number of pages5
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 VI
Publication statusPublished - 2004
Externally publishedYes
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: 2004 Sep 12004 Sep 5

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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