Applying Fuzzy Mathematical Model of Emotional Learning for EEG Signal Classification Between Schizophrenics and Control Participant

This paper concerns the diagnosis of schizophrenia using an electroencephalogram signals, and introduces a new framework based on the brain emotional learning model that can be applied in wide varieties of artificial intelligence applications. We propose the extended supervised version of the neuro-based computational model of an emotional learning referred as to the decay brain emotional learning based fuzzy inference system (DBELFIS). This architecture is based on fuzzy inference system, and it is build-up from the fusion algorithm based on brain emotional learning and fuzzy inference system. In this paper, we compared the proposed method with Multi-layer Perceptron (MLP), brain emotional learning (BEL) and a limbic based artificial emotional neural network (LiAENN). Substantial experimental results show that the proposed approach can effectively diagnose schizophrenia.