Extracting duration information in a picture category decoding task using hidden Markov Models. (9th February 2016)

• Record Type: Journal Article
• Authors: Pfeiffer, Tim; Heinze, Nicolai; Frysch, Robert; Deouell, Leon Y; Schoenfeld, Mircea A; Knight, Robert T; Rose, Georg
• Is Part Of: Journal of neural engineering. Volume 13:Number 2(2016:Apr.)
• Publication Date: 2016-02-09
• Subjects: brain–computer-interfaces -- classification -- hidden-Markov-models -- electrocorticography -- magnetoencephalography -- support-vector-machines; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2560/13/2/026010
• Languages: English
• ISSNs: 1741-2560

Abstract

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.