@article{pfeiffer_extracting_2016,
	title = {Extracting duration information in a picture category decoding task using hidden {Markov} {Models}},
	volume = {13},
	url = {http://stacks.iop.org/1741-2552/13/i=2/a=026010},
	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.},
	number = {2},
	journal = {Journal of Neural Engineering},
	author = {Pfeiffer, Tim and Heinze, Nicolai and Frysch, Robert and Deouell, Leon Y. and Schoenfeld, Mircea A. and Knight, Robert T. and {Georg Rose}},
	year = {2016},
	pages = {026010}
}