Adaptive SVM-Based Classification Increases Performance of a MEG-Based Brain-Computer Interface (BCI)

One problem in current Brain-Computer Interfaces (BCIs) is non-stationarity of the underlying signals. This causes deteriorating performance throughout a session and difficulties to transfer a classifier from one session to another, which results in the need of collecting training data every session. Using an adaptive classifier is one solution to keep the performance stable and reduce the amount of training that is needed for a good BCI performance. In this paper we present an approach for an adaptive classifier based on a Support Vector Machine (SVM). We evaluate its advantage on offline BCI data and show its benefits and online feasibility in an online experiment using a MEG-based BCI with 10 subjects.