EEG complexity analysis has recently been shown to help to diagnose Alzheimer’s Disease (AD) in the early stages. The complexity study is based on the processing of continuous artifact-free Electroencephalography (EEG). Therefore, artifact rejection is normally required because artifacts might mimic cognitive or pathologic activity and therefore bias the neurologist visual interpretation of the EEG. Furthermore, the EEG complexity analysis is strongly altered by artifacts. In this paper, we evaluate the effects of artifacts rejection by a promising technique, Automatic Wavelet-Independent Component Analysis (AWICA), on the EEG Complexity in AD patients. We also investigate the EEG complexity before and after artifact rejection through some measures based on Shannon’s Entropy, Renyi’s Entropy and Tsallis’s Entropy.
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