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There is a high demand for objective indicators in diagnosis of depression as diagnosis of depression is still based on psychiatrist's subjective judgment. A nonlinear method Lempel Ziv Complexity (LZC) has been previously successfully used for detection of neuronal or mental disorders based on electroencephalographic (EEG) signals. However, the method overlooks the high frequency content of EEG signals. Therefore, this study is aimed to find out whether the use of Multiscale Lempel Ziv Complexity (MLZC), considering also high frequencies, could overcome the limitations of LZC and better differentiate depression. In current study the EEG recordings were carried out on the groups of depressive and healthy subjects of 11 volunteers each. The LZC and MLZC were calculated on resting EEG signals in eyes open condition from 30 channels at a length of 2 minutes. The results revealed the incapability of traditional LZC to differentiate depressive subjects from healthy controls in eyes open condition, while MLZC differentiated two groups in numerous channels at different frequencies, giving the highest classification accuracy in channel F3 (86 %) at frequencies 9 and 15.5 Hz. The results indicate that the high frequency information, which is lost in calculation of traditional LZC, has a great value in differentiating between depressive and control groups.

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