Multiple Kernel Learning for Brain-Computer Interfacing
This work addresses the challenge of small sample sizes in BCI by enabling more effective multi-subject learning, though it appears incremental as it applies an existing method to a specific domain.
The paper tackled the problem of improving classification accuracy in Brain-Computer Interfacing by integrating data from multiple subjects or sessions, using Multiple Kernel Learning to simultaneously learn the classifier and optimal weighting, resulting in performance improvements compared to baseline approaches.
Combining information from different sources is a common way to improve classification accuracy in Brain-Computer Interfacing (BCI). For instance, in small sample settings it is useful to integrate data from other subjects or sessions in order to improve the estimation quality of the spatial filters or the classifier. Since data from different subjects may show large variability, it is crucial to weight the contributions according to importance. Many multi-subject learning algorithms determine the optimal weighting in a separate step by using heuristics, however, without ensuring that the selected weights are optimal with respect to classification. In this work we apply Multiple Kernel Learning (MKL) to this problem. MKL has been widely used for feature fusion in computer vision and allows to simultaneously learn the classifier and the optimal weighting. We compare the MKL method to two baseline approaches and investigate the reasons for performance improvement.