EEG Complexity Modifications and Altered Compressibility in Mild Cognitive Impairment and Alzheimer’s Disease Domenico Labate 1,2⋆ , Fabio La Foresta 1 , Isabella Palamara 1 , Giuseppe Morabito 3 , Alessia Bramanti 4 , Zhilin Zhang 5 , and Francesco C. Morabito 1 1 DICEAM - Mediterranea University of Reggio Calabria, Italy 2 DIMES - University of Calabria, Cosenza, Italy 3 University of Pavia, Italy 4 IRCCS Centro Neurolesi bonino Pulejo, Messina, Italy 5 Department of Electrical & Computer Engineering, University of California, San Diego, United States. {domenico.labate,fabio.laforesta,isabella.palamara,morabito}@unirc.it; peppe_mb@hotmail.it;alessia.bramanti@gmail.com;zhangzlacademy@gmail.com Abstract. The objective of this work is to respond to the question: can quantitative electroencephalography (EEG) distinguish among Alzheimer’s Disease (AD) patients, mild cognitive impaired (MCI) subjects and el- derly healthy controls? In other words, are there nonlinear indexes ex- tracted from raw EEG data that are able to manifest the background dif- ference among EEG? The response we give here is that a synthetic index of entropic complexity (Permutation Entropy, PE) as well as a measure of compressibility of the EEG can be used to discriminate among classes of subjects. An experimental database has been analyzed to make these measurements and the results we achieved are encouraging also in terms of disease evolution. Indeed, it is clearly shown that the condition of MCI has intermediate properties with respect to the analyzed markers: thus, these markers could in principle be used to evaluate the probability of transition from MCI to mild AD. Keywords: EEG, Alzheimer’s Disease, Compressive Sensing, Permuta- tion Entropy. 1 Introduction Alzheimer’s disease (AD) is one of the most frequent disorders among elderly population. It is characterized by neural loss and by an accumulation of neurofib- rillary tangles composed of τ -amyloid fibrils and senile β-amyloid (Aβ) plaques [1]. The presence of synaptic loss and neurodegeneration lead to distorted signal ⋆ Corresponding Author: Domenico Labate, DICEAM - Mediterranea University of Reggio Calabria, via Graziella Feo di Vito, 89122 Reggio Calabria, ITALY and DIMES - University of Calabria, Cosenza, ITALY