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.
Olaf Blanke, Oliver Alan Kannape, Hyeongdong Park, Coline Barnoud, Henri Dan Trang
Olaf Blanke, José del Rocio Millán Ruiz, Ronan Boulic, Bruno Herbelin, Ricardo Andres Chavarriaga Lozano, Fumiaki Iwane
Kevin Sivula, Mounir Driss Mensi, Liang Yao, Nestor Guijarro Carratala, Dan Ren, Yongpeng Liu, Meng Xia