Authors:
Lieven Billiet
1
;
Borbála Hunyadi
1
;
Vladimir Matic
1
;
Sabine Van Huffel
1
;
Michel Verleysen
2
and
Maarten De Vos
3
Affiliations:
1
KU Leuven and iMinds, Belgium
;
2
Université Catholique de Louvain, Belgium
;
3
Carl von Ossietzky University, Germany
Keyword(s):
Single-trial ERP BCI, Mobile BCI, Tensors, Subspaces.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Detection and Identification
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Subspace methods have been applied in various application fields to obtain robust results. Using multilinear algebra, they can also be applied on structured tensorial data. This work combines this principle with the power of non-linear kernels to investigate its merits in single trial classification for a mobile BCI ERP classification task. The accuracy difference with regard to more conventional vector kernels is evaluated for sitting and walking condition, increasing training data set and averaging over multiple trials. The study concludes that in general, the tensorial approach does not yield any advantage, though it might for specific subjects.