We examine the use of hidden Markov and hidden semi-Markov models for automatically segmenting an electrocardiogram waveform into its constituent waveform features. An undecimated wavelet transform is used to generate an overcomplete representation of the signal that is more appropriate for subsequent modelling. We show that the state durations implicit in a standard hidden Markov model are ill...

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the traditional HMM. However, in many settings the HDP-HMM’s strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can extend the HDP-HMM to capture such structure by drawing upon explicit...

In this study, we investigated the neural correlates of a visual working memory task. Two experiments were carried out using scalp electroencephalography (EEG) and Electrocorticography (ECoG), respectively. In each trial, participants judged whether a test face had been among a small set of recently studied faces. We used a combination of hidden semi-Markov models (HSMMs) and multi-variate patt...