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.


Published in:
Proceedings of ESANN 2006
Presented at:
European Symposium on Artificial Neural Networks, Bruges, April 26-27-28, 2006
Year:
2006
Keywords:
Laboratories:




 Record created 2006-06-14, last modified 2018-01-27

External link:
Download fulltext
n/a
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)