An automatic algorithm for stationary segmentation of extracellular microelectrode recordings.

Extracellular microelectrode recordings (MER) often contain artifact from a variety of sources that confound traditional signal-processing techniques that require stationary signal segments. We designed an algorithm to locate the longest stationary segment of MER signals. In this paper we provide a description of the segmentation algorithm and its performance assessment. Simulation results demonstrate that the automatic segmentation algorithm we proposed is capable of accurately identifying the boundaries of the longest stationary segments in MER signals. In our simulation study the segmentation algorithm correctly identified the boundaries of the longest MER stationary segments in 99.5% of the cases.