Conference paper

Spatial filters for the classification of event-related potentials

Spatial filtering is a widely used dimension reduction method in electroencephalogram based brain-computer interface systems. In this paper a new algorithm is proposed, which learns spatial filters from a training dataset. In contrast to existing approaches the proposed method yields spatial filters that are explicitly designed for the classification of event-related potentials, such as the P300 or movement-related potentials. The algorithm is tested, in combination with support vector machines, on several benchmark datasets from past BCI competitions and achieves state of the art results.

    Keywords: LTS1


    • LTS-CONF-2006-010

    Record created on 2006-06-14, modified on 2017-05-10

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