Extracting latent brain states--Towards true labels in cognitive neuroscience experiments.

Journal: NeuroImage
Published Date: Jun 9, 2015

Abstract

Neuroscientific data is typically analyzed based on the behavioral response of the participant. However, the errors made may or may not be in line with the neural processing. In particular in experiments with time pressure or studies where the threshold of perception is measured, the error distribution deviates from uniformity due to the structure in the underlying experimental set-up. When we base our analysis on the behavioral labels as usually done, then we ignore this problem of systematic and structured (non-uniform) label noise and are likely to arrive at wrong conclusions in our data analysis. This paper contributes a remedy to this important scenario: we present a novel approach for a) measuring label noise and b) removing structured label noise. We demonstrate its usefulness for EEG data analysis using a standard d2 test for visual attention (N=20 participants).

Authors

  • Anne K Porbadnigk
    Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany.
  • Nico Görnitz
    Machine Learning Group, Technical University of Berlin, Berlin, Germany.
  • Claudia Sannelli
    Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany.
  • Alexander Binder
    Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany.
  • Mikio Braun
    Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany.
  • Marius Kloft
    Department of Computer Science, Humboldt University of Berlin, Berlin, Germany.
  • Klaus-Robert Müller
    Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Deutschland.

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