Extracting duration information in a picture category decoding task using hidden Markov Models

Tim Pfeiffer, Nicolai Heinze, Robert Frysch, Leon Y. Deouell, Mircea A. Schoenfeld, Robert T. Knight, Georg Rose

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Objective. Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach. Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results. Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance. The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.

Original languageAmerican English
Article number026010
JournalJournal of Neural Engineering
Volume13
Issue number2
DOIs
StatePublished - 9 Feb 2016

Bibliographical note

Publisher Copyright:
© 2016 IOP Publishing Ltd.

Keywords

  • brain-computer-interfaces
  • classification
  • electrocorticography
  • hidden-Markov-models
  • magnetoencephalography
  • support-vector-machines

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