EDHMM classification produces improved agreement between simultaneously recorded LFP and MP signals.

Abstract

<p><b>A</b>) Example of an LFP (blue trace) recorded simultaneously with the MP (black trace) of a nearby cortical neuron. The corresponding LFP (gray) and MP (red) state sequences inferred from the EDHMM are overlaid. <b>B</b>) The instantaneous probability of detecting false DOWN states is plotted against that for false UP states for the EDHMM method as well as the SMM- and Np-TC methods. The mean and SEM are indicated by the colored crosses. <b>C</b>) The probability of a missed LFP state, relative to the MP state sequence, is plotted against the probability of detecting an extra LFP state. <b>D</b>) Box plots illustrating the changes in <i>e_i</i> relative to the EDHMM algorithm for the following decoding algorithms: HMM; fixed-mean EDHMM (fm-EDHMM); static mixture model threshold-crossing (SMM-TC); and nonparametric threshold-crossing (Np-TC). <b>E</b>) Same as D for <i>e_s</i>.</p