@inproceedings{4247,
  title = {Adapting Spatial Filter Methods for Nonstationary BCIs},
  journal = {Proceedings of 2006 Workshop on Information-Based Induction Sciences (IBIS 2006)},
  booktitle = {IBIS 2006},
  abstract = {A major challenge in applying machine learning methods to Brain-Computer
  Interfaces (BCIs) is to overcome the possible nonstationarity in the data from the datablock
  the method is trained on and that the method is applied to. Assuming the joint
  distributions of the whitened signal and the class label to be identical in two blocks, where
  the whitening is done in each block independently, we propose a simple adaptation formula
  that is applicable to a broad class of spatial filtering methods including ICA, CSP, and
  logistic regression classifiers. We characterize the class of linear transformations for which
  the above assumption holds. Experimental results on 60 BCI datasets show improved
  classification accuracy compared to (a) fixed spatial filter approach (no adaptation) and
  (b) fixed spatial pattern approach (proposed by Hill et al., 2006 [1]).},
  pages = {65-70},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = nov,
  year = {2006},
  slug = {4247},
  author = {Tomioka, R. and Hill, JN. and Blankertz, B. and Aihara, K.},
  month_numeric = {11}
}