Extracting duration information in a picture category decoding task using Hidden Markov Models

ABSTRACT

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.

AUTHORS

• Tim Pfeiffer

• Nicolai Heinze

• Robert Frysch

• Leon Y. Deouell

• Mircea Schoenfeld

• Robert T. Knight

• Georg Rose

Date: 2016

DOI: doi:10.1088/1741-2560/13/2/026010

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Support vector machine and hidden Markov model based decoding of finger movements using electrocorticography

Authors:

• Tobias Wissel

• Tim Pfeiffer

• Robert Frysch

• Robert T. Knight

• Edward F. Chang

• Hermann Hinrichs

• Jochem W. Rieger

• Georg Rose

Date: 2013

DOI: 10.1088/1741-2560/10/5/056020

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Abstract:

Objective. Support vector machines (SVM) have developed into a gold standard for accurate classification in brain–computer interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of hidden Markov models (HMM) for online BCIs and discuss strategies to improve their performance. Approach. We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from electrocorticograms of four subjects performing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. Main results. We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. Significance. We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online BCIs.