Authors
Ulrich Hoffmann, J-M Vesin, Touradj Ebrahimi
Publication date
2006
Journal
Proceedings of ESANN 2006
Description
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.
Total citations
Scholar articles
U Hoffmann, JM Vesin, T Ebrahimi - Proceedings of ESANN 2006, 2006