@inproceedings{6504,
  title = {Multitask Learning for Brain-Computer Interfaces},
  journal = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010},
  abstract = {Brain-computer interfaces (BCIs) are limited
  in their applicability in everyday settings
  by the current necessity to record subjectspecific
  calibration data prior to actual use
  of the BCI for communication. In this paper,
  we utilize the framework of multitask
  learning to construct a BCI that can be used
  without any subject-specific calibration process.
  We discuss how this out-of-the-box BCI
  can be further improved in a computationally
  efficient manner as subject-specific data
  becomes available. The feasibility of the approach
  is demonstrated on two sets of experimental
  EEG data recorded during a standard
  two-class motor imagery paradigm from
  a total of 19 healthy subjects. Specifically,
  we show that satisfactory classification results
  can be achieved with zero training data,
  and combining prior recordings with subjectspecific
  calibration data substantially outperforms
  using subject-specific data only. Our
  results further show that transfer between
  recordings under slightly different experimental
  setups is feasible.},
  pages = {17-24},
  editors = {Teh, Y.W. , M. Titterington},
  publisher = {JMLR},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = may,
  year = {2010},
  slug = {6504},
  author = {Alamgir, M. and Grosse-Wentrup, M. and Altun, Y.},
  month_numeric = {5}
}