Reza Yaghoobi Karimoi

Work place: Department of Biomedical Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran



Research Interests: Computer systems and computational processes, Computational Learning Theory, Neural Networks, Systems Architecture, Data Structures and Algorithms


Reza Yaghoobi Karimoi He received the B.S. degree in electronics engineering from the Islamic Azad University, Yazd Branch, Iran, in 2009, the M.S. degree at biomedical engineering from the University of Islamic Azad University, Mashhad Branch, Mashhad, Iran, in 2011.

His research interests include modeling of biological systems, biofeedback and neurofeedback, linear and non-linear computing, machine learning and neural networks.

Author Articles
The Effects of Beta-I and Fractal Dimension Neurofeedback on Reaction Time

By Reza Yaghoobi Karimoi Azra Yaghoobi Karimoi

DOI:, Pub. Date: 8 Oct. 2014

In this paper, we evaluate the effects of neurofeedback training protocols of the relative power of the beta-I band and the fractal dimension on the reaction time of human by the Test of Variables of Attention (TOVA) to show which of these two protocols have the great ability for the improving of the reaction time. The findings of this research show that both protocols have a good ability (p < 0.01) to improving of the reaction time and can create the significant difference (as mean dRT = 37.3 ms for the beta-I protocol and dRT = 19.6 ms for the fractal protocol) in the reaction time. Of course, we must express, the Beta-I protocol has the more ability to improving of the reaction time and it is able to provide a faster reaction time.

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Classification of EEG signals using Hyperbolic Tangent - Tangent Plot

By Reza Yaghoobi Karimoi Azra Yaghoobi Karimoi

DOI:, Pub. Date: 8 Jul. 2014

In this paper, a novel signal processing method is suggested for classifying epileptic seizures. To this end, first the Tangent and Hyperbolic Tangent of signals are calculated and then are classified into two classes: normal (or interictal) and ictal, using a proposed classifier. The results of this method show that the classification accuracy of normal and ictal classes (97.41%) has been higher than interictal and ictal classes (92.83%) and generally, it has a good potential to become a useful tool for physicians.

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Other Articles