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Dementia diseases are increasing rapidly, according to the World Health Organization (WHO), becoming an alarming problem for the health sector. The electroencephalogram (EEG) is a non-invasive study that records brain electrical activity and has a wide field of applications in the medical area, one of which is the detection of neurodegenerative diseases. The objective of this work is to present the results of a thorough review of the use of EEG systems for the detection of dementia diseases. Around 82 published papers between 2009 and 2020 were reviewed, and compared among them obtaining data such as sampling time, the number of electrodes, the most popular processing, classification, and validation techniques, as well as an analysis of the reported results. The relationship of the selected parameters with the efficiency obtained is shown, some more common combinations in the reviewed articles that demonstrated to have reliability levels greater than 90% and details to be considered at each stage of the process. An overview of the most commonly used classification tools and processing techniques is also described.

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