title: Multisubject learning for common spatial patterns in motor-imagery BCI.
abstract: Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.
authors: Electrical Energy, Systems and Automation, Ghent University, Gent, Belgium. ddvlamin@gmail.com
authors: Dieter
authors: D
authors: Devlaminck
authors: Bart
authors: B
authors: Wyns
authors: Moritz
authors: M
authors: Grosse-Wentrup
authors: Georges
authors: G
authors: Otte
authors: Patrick
authors: P
authors: Santens
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007092:Imagination / Q000502:physiology*; D009043:Motor Activity / Q000502:physiology*; D014584:User-Computer Interface