Segmentation of extracellular microelectrode recordings with equal power

We describe a segmentation algorithm for extracellular microelectrode recordings identifying segments with equal power. The observed temporal correlation of the microelectrode data was accounted for by estimating the variance of the autocovariance of the segments. The iterative method of comparing segments and determining abrupt changes in the power was validated on synthetic signals and microelectrode recording data acquired at different neuronal structures along the stereotactic trajectory in patients with Parkinson disease.