| Abstract: |  This bachelor project deals with analysis of neurological data using regularity measures. The signals are microelectrode recordings acquired during deep brain stimulation surgery. This project's aim is find suitable measure for classifi cation of basal ganglia's cores, namely subthalamic nucleus, globus pallidus and substantia nigra, and adjacent parts of thalamus. Capability of an automatic signal classi cation would be a considerable benefi t during operations, where it is necessary to locate cores precisely. All measures passed ANOVA/Kruskal-Wallis test on significance level α = 0.05 while the best discrimination in term of p-value was achieved for sample entropy. Two used regularity measures, Lempel-Ziv complexity and sample entropy provided good results to discriminate different basal ganglia nuclei.
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