title: Extracting latent brain states--Towards true labels in cognitive neuroscience experiments.
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.affiliation: Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany.
authors.forename: Anne K
authors.initials: AK
authors.lastname: Porbadnigk
authors.affiliation: Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany.
authors.forename: Nico
authors.initials: N
authors.lastname: Görnitz
authors.affiliation: Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany.
authors.forename: Claudia
authors.initials: C
authors.lastname: Sannelli
authors.affiliation: Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Binder
authors.affiliation: Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany.
authors.forename: Mikio
authors.initials: M
authors.lastname: Braun
authors.affiliation: Department of Computer Science, Humboldt University of Berlin, Berlin, Germany.
authors.forename: Marius
authors.initials: M
authors.lastname: Kloft
authors.affiliation: Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea. Electronic address: klaus-robert.mueller@tu-berlin.de.
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D000328:Adult; D001288:Attention / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D000066494:Cognitive Neuroscience / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010364:Pattern Recognition, Visual; D000069558:Unsupervised Machine Learning; D055815:Young Adult
keywords: Brain-computer interfaces; EEG; Systematic label noise; Unsupervised learning