Work place: Electrical Engineering Department Ferdowsi University of Mashhad, Mashhad, Iran
E-mail: naghib@yahoo.com
Website:
Research Interests: Mathematics of Computing, Computational Learning Theory
Biography
Mohammad Bagher Naghibi-Sistani received the Ph.D. degree in electrical engineering from the Ferdowsi University of Mashhad, Iran. He currently is Assistant Professor at the Department of Electrical Engineering, Ferdowsi University of Mashhad. His research interests include reinforcement learning, soft computing, Machine learning. He has authored or coauthored over 50 journal and conference papers.
By Seyyed Abed Hosseini Mohammad Bagher Naghibi-Sistani
DOI: https://doi.org/10.5815/ijigsp.2011.05.05, Pub. Date: 8 Aug. 2011
This paper proposes an emotion recognition system using EEG signals, therefore a new approach to emotion state analysis by approximate (ApEn) and wavelet entropy (WE) is described. We have used EEG signals recorded during emotion in five channels (FP1, FP2, T3, T4 and Pz), under pictures induction environment (calm-neutral and negative excited) for participants. After a brief introduction to the concept, the ApEn and WE were extracted from two different EEG time series. The result showed that, the classification accuracy in two emotion states was 73.25% using the support vector machine (SVM) classifier. The simulations showed that the classification accuracy is good and the proposed methods are effective. During an emotion, the EEG is less complex compared to the normal, indicating reduction in active neuronal process in the brain.
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