When brain and behavior disagree: Tackling systematic label noise in EEG data with machine learning

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

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

Conventionally, neuroscientific data is analyzed based on the behavioral response of the participant. This approach assumes that behavioral errors of participants are in line with the neural processing. However, this may not be the case, in particular in experiments with time pressure or studies investigating the threshold of perception. In these cases, the error distribution deviates from uniformity due to the heteroscedastic nature of the underlying experimental set-up. This problem of systematic and structured (non-uniform) label noise is ignored when analysis are based on behavioral data, as is being done typically. Thus, we run the risk to arrive at wrong conclusions in our analysis. This paper proposes a remedy to handle this crucial problem: we present a novel approach for a) measuring label noise and b) removing structured label noise. We show its usefulness for an EEG data set recorded during a standard d2 test for visual attention.

Original languageEnglish
DOIs
Publication statusPublished - 2014
Event2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 - Gangwon, Korea, Republic of
Duration: 2014 Feb 172014 Feb 19

Other

Other2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
CountryKorea, Republic of
CityGangwon
Period14/2/1714/2/19

Keywords

  • Applied Cognitive Neuroscience
  • EEG
  • Label Noise
  • Machine Learning
  • Unsupervised Learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Human Factors and Ergonomics

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  • Cite this

    Porbadnigk, A. K., Görnitz, N., Sannelli, C., Binder, A., Braun, M., Kloft, M., & Müller, K. R. (2014). When brain and behavior disagree: Tackling systematic label noise in EEG data with machine learning. Paper presented at 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014, Gangwon, Korea, Republic of. https://doi.org/10.1109/iww-BCI.2014.6782561