The rapid increase in the number of older people with Alzheimer’s disease (AD) and other forms of dementia represents one of the major challenges to the health and social care systems because of a large number of people affected. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new treatments and therapies, as and when they become available, and to plan for the future. The onset of AD starts many years before the clinical symptoms become clear. A biomarker that can measure the brain changes in this period would be useful for early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in early detection of AD. Damage caused to the brain due to AD leads to changes in the information-processing activity of the brain which can be quantified by the EEG and used as a biomarker of AD.EEG provides useful insight into brain functions and can play a useful role as the first line of a decisionsupport tool for early detection and diagnosis of dementia. It is non-invasive, low-cost and has a high temporal resolution. EEG is suitable to develop a tool can be used in general practice to detect people at high risk of AD. Tsallis Entropy (TsEn) [1], changes in the EEG amplitude (ΔEEGA) [2], and Higuchi fractal dimension (HFD) [3] have been shown to be the most promising methods for quantifying changes in the EEG due to AD. In this study, we analyzed the efficacy of using EEG biomarkers of AD extracted from these three promising methods. The results show that AD patients have a significant reduction in TsEn, ΔEEGA, and HFD values. This reduction is significant enough to allow the discrimination between AD patients and normal subjects based on these biomarkers