title: Extracting duration information in a picture category decoding task using hidden Markov Models.
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.
  
  
  METHODS
  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.
  
  
  RESULTS
  Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only.
  
  
  CONCLUSIONS
  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.
authors: Institute for Medical Engineering, Otto-von-Guericke-University Magdeburg, Germany.
authors: Tim
authors: T
authors: Pfeiffer
authors: Nicolai
authors: N
authors: Heinze
authors: Robert
authors: R
authors: Frysch
authors: Leon Y
authors: LY
authors: Deouell
authors: Mircea A
authors: MA
authors: Schoenfeld
authors: Robert T
authors: RT
authors: Knight
authors: Georg
authors: G
authors: Rose
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D000069280:Electrocorticography / Q000379:methods; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D015225:Magnetoencephalography / Q000379:methods; D008297:Male; D008390:Markov Chains; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods*; D011597:Psychomotor Performance / Q000502:physiology*; D011897:Random Allocation