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Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Population-physic-based Algorithm

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Abstract

A brain-computer interface (BCI) system is one of the most effective ways that translates brain signals into output commands. Different imagery activities can be classified based on the changes in μ and β rhythms and their spatial distributions. Multi-layer perceptron neural networks (MLP-NNs) are commonly used for classification. Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently. Conventional methods for training NNs, such as gradient descent and recursive methods, have some disadvantages including low accuracy, slow convergence speed and trapping in local minimums. In this paper, in order to overcome these issues, the MLP-NN trained by a hybrid population-physics-based algorithm, the combination of particle swarm optimization and gravitational search algorithm (PSOGSA), is proposed for our classification problem. To show the advantages of using PSOGSA that trains NNs, this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization (PSO), gravitational search algorithm (GSA) and new versions of PSO. The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics. The results show that the proposed algorithm in most subjects of encephalography (EEG) dataset has very better or acceptable performance compared to others.

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Authors and Affiliations

  1. Department of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran

    Sajjad Afrakhteh, Mohammad-Reza Mosavi, Mohammad Khishe & Ahmad Ayatollahi

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  1. Sajjad Afrakhteh
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  2. Mohammad-Reza Mosavi
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  3. Mohammad Khishe
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  4. Ahmad Ayatollahi
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Correspondence to Mohammad-Reza Mosavi.

Additional information

Sajjad Afrakhteh received the B. Sc. degree in electrical engineering from Shiraz University of Science and Technology, Iran in 2015, and the M. Sc. degree in digital electronic from Iran University of Science and Technology (IUST), Iran in 2017. He is currently a Ph.D. degree candidate in electronic engineering, IUST, Iran. His research interests include system and circuit design, signal processing, image processing, artificial neural networks and meta-heuristic algorithms.

Mohammad-Reza Mosavi received the B. Sc., M. Sc. and Ph. D. degrees in electronic engineering from Iran University of Science and Technology, Iran in 1997, 1998, and 2004, respectively. He is currently faculty member (full professor) of the Department of Electrical Engineering, IUST, Iran. He is the author of more than 330 scientific publications in journals and international conferences. His research interests include circuits and systems design.

Mohammad Khishe received the B. Sc. degree in maritime electronic and communication engineering from Imam Khomeini Maritime Sciences University, Iran in 2007, the M. Sc. and Ph. D. degrees in electronic engineering from Islamic Azad University, Qazvin branch and Iran University of Science and Technology, Tehran in 2012 and 2017, respectively. Since 2011, he has been a faculty member of Maritime Electrical and Electronic Engineering at Imam Khomeini Maritime Sciences University, Iran. His research interests include digital signal processing, artificial neural networks, meta-heuristic algorithms, sonar and radar signal processing, and field-programmable gate array (FPGA) design.

Ahmad Ayatollahi received the B. Sc. degree in electrical engineering from Iran University of Science and Technology, Iran in 1976, and the M. Sc. and Ph.D. degrees in instrumentation engineering and electrical engineering from University of Manchester Institute of Science and Technology (UMIST), UK in 1985 and 1989, respectively. He is currently faculty member (associate professor) of the College of Electrical Engineering, Iran University of Science and Technology, Tehran. His research interests include medical image and signal processing, design of electronic circuits, power electronics, and ultrasound.

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Afrakhteh, S., Mosavi, MR., Khishe, M. et al. Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Population-physic-based Algorithm. Int. J. Autom. Comput. 17, 108–122 (2020). https://doi.org/10.1007/s11633-018-1158-3

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