The aim of the present study was to develop valuable and reliable indices of post-stroke dementia particularly vascular dementia (VaD) using entropy-based features extracted from the electroencephalography (EEG) background activity of 5 VaD patients, 15 stroke-related patients with mild cognitive impairment (MCI) and 15 control healthy subjects during a working memory (WM) task. EEG artifacts were removed using independent component analysis and wavelets (AICA-WT). Using ANOVA (p< 0.05), spectral entropy (SpecEn) was used to test the hypothesis that the EEG signal slows down in both VaD and MCI in comparison with control subjects whereas the permutation entropy (PerEn) and tsallis entropy (TsEn) features were used to test the hypothesis that the complexity in both VaD and MCI were reduced in comparison with control subjects. SpecEn reflected the slowing in the brain activity in VaD and MCI patients whereas PerEn and TsEn results in reducing the complexity in VaD and MCI patients. Therefore, EEG could be as a reliable index for inspecting the background activity in the identification of patients with VaD and stroke-related MCI.