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Multi-subject learning for common spatial patterns in motor-imagery BCI

2011

Article

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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.

Author(s): Devlaminck, D. and Wyns, B. and Grosse-Wentrup, M. and Otte, G. and Santens, P.
Journal: Computational Intelligence and Neuroscience
Volume: 2011
Number (issue): 217987
Pages: 1-9
Year: 2011
Month: August
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1155/2011/217987

Links: PDF

BibTex

@article{DevlaminckWGOS2011,
  title = {Multi-subject learning for common spatial patterns in motor-imagery BCI},
  author = {Devlaminck, D. and Wyns, B. and Grosse-Wentrup, M. and Otte, G. and Santens, P.},
  journal = {Computational Intelligence and Neuroscience},
  volume = {2011},
  number = {217987},
  pages = {1-9},
  month = aug,
  year = {2011},
  month_numeric = {8}
}