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Alzheimer's disease (AD) is the most common type of dementia, greatly affecting cognitive functioning and independent living of elderly population. The lack of an available drug therapy that could prevent disease progression shifted the research interest towards the early detection of the neurodegeneration symptoms that affect the mature brain and impair the interaction between brain regions, thus partially causing functional disconnection. The notion of electroencephalographic complexity is a valid and reliable method of quantifying the degree of isolation of brain regions due to AD pathology. Recently permutation entropy, which is a methodology of transforming the signal data into symbolic sequences and then computing the frequency distribution of symbolic patterns, gained great attention and was applied in seizure detection and computation of consciousness. The current study aims to investigate whether this complexity marker would be suitable to be applied in dementia research towards the quantification of the degree of cognitive deterioration due to disconnection of brain regions. The promising results indicate that permutation entropy on posterior regions (parieto-occipital areas) abnormally increases during mild dementia and is negatively correlated with the level of cognitive dysfunction, as estimated by the Mini Mental State Examination. Therefore, it may be a fast, accurate and simple tool for screening elderly population prone in Alzheimer.