Benchmarking framework for machine learning classification from fNIRS data.

Journal: Frontiers in neuroergonomics
Published Date:

Abstract

BACKGROUND: While efforts to establish best practices with functional near infrared spectroscopy (fNIRS) signal processing have been published, there are still no community standards for applying machine learning to fNIRS data. Moreover, the lack of open source benchmarks and standard expectations for reporting means that published works often claim high generalisation capabilities, but with poor practices or missing details in the paper. These issues make it hard to evaluate the performance of models when it comes to choosing them for brain-computer interfaces.

Authors

  • Johann Benerradi
    School of Computer Science, University of Nottingham, Nottingham, United Kingdom.
  • Jeremie Clos
    School of Computer Science, University of Nottingham, Nottingham, Nottinghamshire UK.
  • Aleksandra Landowska
    School of Computer Science, University of Nottingham, Nottingham, United Kingdom.
  • Michel F Valstar
    School of Computer Science, University of Nottingham, Nottingham, United Kingdom.
  • Max L Wilson
    School of Computer Science, University of Nottingham, Nottingham, United Kingdom.

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