Electroencephalography (EEG) is widely used technique to investigate the neuropathology of patients using noninvasive physiological monitoring as well as in research and clinical investigation. EEG is noninvasive testing method containing diverse information regarding different physiological and pathological states of human brain and thus considered as effective tool to understand the complex behavior of brain. EEG motor movement/ imagery commonly used as cognitive nature of methodology that involve the mental simulation of motor related tasks such as eye open and close, left and right fist and finger movement and other motor executive brain regions. Multiscale entropy analysis (MSE) recently developed technique is used to quantify the dynamics of physiological signals at different time scales and discriminate the EEG motor movement task of eye open and close signals recorded using 10-20 EEG system. EEG recordings of Motor Movement/ Imagery tasks were taken from the publically available Physionet database acquired using 64 electrodes. The MSE analysis was performed on the EEG data acquired from all the electrodes of Eye Open and Close tasks. Mann-Whitney rank test was used to find significant differences between the groups and result were considered statistically significant for p-values<0.05. To find the degree of separation among the groups, the area under receiver operator curve was computed. Mean ranks for MSE are computed for all electrodes and higher complexity was obtained for higher ranks and vice versa. The finding indicated that EEG signals acquired through electrodes F2, F3, F4, F5, F6, F7, F8, Fp1, Fp2, FC1, FC2, O1, O2, P3, P4, C3 and C4 showed significant differences between Eye open and Eye close tasks at time scales 1 to 10. Moreover, the highest accuracy and separation was obtained at front regions, central region and front polar regions.

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