Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2006 | ESANN | spatial filtering,support vector machine,brain computer interface,event related potential,dimension reduction |
Field | DocType | Citations |
Dimensionality reduction,Pattern recognition,Computer science,Brain–computer interface,Support vector machine,Event-related potential,Artificial intelligence,Machine learning,Spatial filter | Conference | 13 |
PageRank | References | Authors |
1.20 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ulrich Hoffmann | 1 | 21 | 3.61 |
| Jean-Marc Vesin | 2 | 201 | 32.09 |
| Touradj Ebrahimi | 3 | 4327 | 322.13 |