Decoding brain states during auditory perception by supervising unsupervised learning

Anne K. Porbadnigk, Nico Görnitz, Marius Kloft, Klaus Muller

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The last years have seen a rise of interest in using electroencephalography-based brain computer interfacing methodology for investigating non-medical questions, beyond the purpose of communication and control. One of these novel applications is to examine how signal quality is being processed neurally, which is of particular interest for industry, besides providing neuroscientific insights. As for most behavioral experiments in the neurosciences, the assessment of a given stimulus by a subject is required. Based on an EEG study on speech quality of phonemes, we will first discuss the information contained in the neural correlate of this judgement. Typically, this is done by analyzing the data along behavioral responses/labels. However, participants in such complex experiments often guess at the threshold of perception. This leads to labels that are only partly correct, and oftentimes random, which is a problematic scenario for using supervised learning. Therefore, we propose a novel supervised-unsupervised learning scheme, which aims to differentiate true labels from random ones in a data-driven way. We show that this approach provides a more crisp view of the brain states that experimenters are looking for, besides discovering additional brain states to which the classical analysis is blind

Original languageEnglish
Pages (from-to)112-121
Number of pages10
JournalJournal of Computing Science and Engineering
Volume7
Issue number2
DOIs
Publication statusPublished - 2013

Keywords

  • Anomaly detection
  • Brain-computer interfacing
  • EEG
  • Semi-supervised learning
  • Systematic label noise

ASJC Scopus subject areas

  • Computer Science Applications
  • Engineering(all)

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