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Assessment and Communication for People with Disorders of Consciousness
07:37

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Recursive N-way partial least squares for brain-computer interface.

Andrey Eliseyev1, Tetiana Aksenova

  • 1CLINATEC, CEA, Grenoble, France. eliseyev.andrey@gmail.com

Plos One
|August 8, 2013
PubMed
Summary

Recursive N-way Partial Least Squares (RNPLS) regression handles large tensor data and time-dependent processes efficiently. This method offers fast convergence and effective Brain Computer Interface calibration for neural activity modeling.

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Area of Science:

  • Multivariate Data Analysis
  • Machine Learning
  • Neuroscience

Background:

  • High-dimensional tensor data presents challenges for traditional regression methods.
  • Adaptive modeling of time-dependent processes requires advanced algorithms.

Purpose of the Study:

  • To introduce and evaluate the blockwise Recursive N-way Partial Least Squares (RNPLS) regression algorithm.
  • To assess the performance of RNPLS for large-scale tensor data and time-varying systems.

Main Methods:

  • The study considers tensor-input/tensor-output blockwise RNPLS regression.
  • It combines multi-way tensor decomposition with a consecutive calculation scheme.
  • The algorithm allows blockwise treatment of large tensor data arrays and adaptive modeling.

Main Results:

  • The numerical study demonstrates fast and stable convergence of regression coefficients for RNPLS.
  • Applied to Brain Computer Interface system calibration, RNPLS efficiently adjusts the decoding model.
  • The algorithm shows effectiveness in modeling multi-modal neural activity flow.

Conclusions:

  • RNPLS is an efficient method for handling large-dimensional tensor data and time-dependent processes.
  • The algorithm provides effective calibration for Brain Computer Interface systems.
  • RNPLS is suitable for various multi-modal neural activity flow modeling tasks due to its online adaptation and interpretability.