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


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
Issue number2
StatePublished - 9 Feb 2016

Bibliographical note

Funding Information:
Acknowledgments: The work of this paper is funded by the Federal Ministry of Education and Research (Germany) within the Forschungscampus STIMULATE under grant number 13GW0095A and supported by grant 2013070 from the USIsrael binational science foundation to LYD and RTK.

Publisher Copyright:
© 2016 IOP Publishing Ltd.


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


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