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Extracting duration information in a picture category decoding task using hidden Markov Models
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DOI：10.1088/1741-2560/13/2/026010.png)
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Abstract
En 中文
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.
Keywords:
brain computer-interfaces
classification
hidden-Markov-models
electrocorticography
magnetoencephalography
support-vector-machines
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