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Title : A theta-band EEG based index for early diagnosis of Alzheimer ' s disease Running title : EEG based index to improve AD diagnosis

Despite recent advances, early diagnosis of Alzheimer's disease from electroencephalography remains a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One feature that has been used for discriminative classification is changes in electroencephalography synchrony. So far, only the decrease of synchrony in the higher frequencies has been deeply analysed. In this paper, we investigate the increase of synchrony found in narrow frequency ranges within the θ band. This particular increase of synchrony is used with the well-known decrease of synchrony in the α band to enhance detectable differences between Alzheimer's patients and healthy subjects. We propose a new synchrony ratio that maximizes the differences between two populations. The ratio is tested using two different data sets, one of them containing Mild Cognitive Impairment patients and healthy subjects, and another one, containing Mild Alzheimer's Disease patients and healthy subjects. The results presented in this paper show that classification rate is improved, and the statistical difference between Alzheimer's patients and healthy subjects is increased using the proposed ratio.