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. 2017 Jan 26;10:604. doi: 10.3389/fnins.2016.00604

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Copyright © 2017 Triggiani, Bevilacqua, Brunetti, Lizio, Tattoli, Cassano, Soricelli, Ferri, Nobili, Gesualdo, Barulli, Tortelli, Cardinali, Giannini, Spagnolo, Armenise, Stocchi, Buenza, Scianatico, Logroscino, Lacidogna, Orzi, Buttinelli, Giubilei, Del Percio, Frisoni and Babiloni.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Structures of the three artificial neural networks (ANNs) used to classify Alzheimer's disease patients with dementia (AD) from Normal elderly subjects (Nold). EEG markers are given as inputs in the first layer (input layer); every node (the numbered circles) of every successive layer (i.e., the hidden layers and the output layer) is characterized by an activation function: A non-linear function to decide, in analogy with biological neurons, the output of the node (0 or 1). The output node (O) provides the classification result (AD or Nold). Legend for the input markers: (top) the four best Lagged Linear Connectivity (LLC) markers; (bottom left) the four best LLC markers together with the four best Source Current Density (SCD) markers; (bottom right) the four best SCD markers. Legend for the activation functions: log-sigmoid (logsig), linear (purelin), and tan-sigmoid (tansig).