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

Anne K. Porbadnigk, Nico Görnitz, Claudia Sannelli, Alexander Binder, Mikio Braun, Marius Kloft, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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).

Original languageEnglish
Pages (from-to)225-253
Number of pages29
Publication statusPublished - 2015


  • Brain-computer interfaces
  • EEG
  • Systematic label noise
  • Unsupervised learning

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


Dive into the research topics of 'Extracting latent brain states - Towards true labels in cognitive neuroscience experiments'. Together they form a unique fingerprint.

Cite this