PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improving speed and accuracy of Brain-Computer Interfaces using readiness potential features
Matthias Krauledat, Guido Dornhege, Benjamin Blankertz, Florian Losch, Gabriel Curio and Klaus-Robert Müller
In: 26th Annual International Conference IEEE EMBS on Biomedicine, 1-5 Sep 2004, San Francisco, USA.

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.

EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:474
Deposited By:Guido Dornhege
Deposited On:23 December 2004