¹College of Information Engineering, Shanghai Maritime University, China
²Department of Automation and Systems Technology, Aalto University School of Electrical Engineering, Finland
³Laboratory of Image Processing and Pattern Recognition, Beijing Normal University, China
⁴Department of Biomedical Engineering, Tampere University of Technology, Finland
⁵School of Foundational Education, Peking University Health Science Center, China

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Abstract—In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Three HS methods, the original version and two new variations recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training.

Index Terms—Harmony Search (HS) method, ElectroEncephaloGram (EEG), BP neural networks, optimization, Opposition-Based Learning (OBL), memetic computing, bee foraging algorithm, signal classification.

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Cite: Xiao-Zhi Gao, Jing Wang, Jarno M. A. Tanskanen, Rongfang Bie, Xiaolei Wang, Ping Guo, Kai Zenger, "Optimal Classification of Epileptic EEG Signals Using Neural Networks and Harmony Search Methods," Journal of Software vol. 9, no. 1, pp. 230-239, 2014.