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Recursive N-Way Partial Least Squares for Brain-Computer Interface

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  • Andrey Eliseyev
  • Tetiana Aksenova

Abstract

In the article tensor-input/tensor-output blockwise Recursive N-way Partial Least Squares (RNPLS) regression is considered. It combines the multi-way tensors decomposition with a consecutive calculation scheme and allows blockwise treatment of tensor data arrays with huge dimensions, as well as the adaptive modeling of time-dependent processes with tensor variables. In the article the numerical study of the algorithm is undertaken. The RNPLS algorithm demonstrates fast and stable convergence of regression coefficients. Applied to Brain Computer Interface system calibration, the algorithm provides an efficient adjustment of the decoding model. Combining the online adaptation with easy interpretation of results, the method can be effectively applied in a variety of multi-modal neural activity flow modeling tasks.

Suggested Citation

  • Andrey Eliseyev & Tetiana Aksenova, 2013. "Recursive N-Way Partial Least Squares for Brain-Computer Interface," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-13, July.
    • Handle: RePEc:plo:pone00:0069962
    DOI: 10.1371/journal.pone.0069962
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    Cited by:

    1. Andrey Eliseyev & Tetiana Aksenova, 2016. "Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.

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