Paraconsistent artificial neural networks and EEG

Issue title: Selected papers of KES2012 - Part 2 of 2

Guest editors: M. Graña, A.I. Gonzalez-Acuña and C. Zanni-Merk

Article type: Research Article

Authors: Abe, Jair Minoro^{a; b; *} | Lopes, Helder F.S.^b | Nakamatsu, Kazumi^c

Affiliations: [a] Graduate Program in Production Engineering, ICET, Paulista University, São Paulo, Brazil | [b] Institute For Advanced Studies, University of São Paulo, São Paulo, Brazil | [c] School of Human Science and Environment (H.S.E.), University of Hyogo, Kobe, Japan | Computational Intelligence Group, University of the Basque Country, UPV/EHU, Spain

Correspondence: [*] Corresponding author: Jair Minoro Abe, Graduate Program in Production Engineering, ICET, Paulista University, R. Dr. Bacelar, 1212, CEP 04026-002 São Paulo, Brazil. E-mail: [email protected]

Abstract: In this work we summarize some of our studies on Paraconsistent Artificial Neural Networks applied to electroencephalography. In particular we've applied in the study of the Alzheimer Disease and Attention-Deficit/Hyperactivity Disorder (ADHD).

Keywords: Artificial neural network, paraconsistent logics, EEG analysis, pattern recognition, Alzheimer disease, dyslexia

DOI: 10.3233/KES-130266

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 17, no. 2, pp. 99-111, 2013