An Empirical Analysis of Training Algorithms of Neural Networks: A Case Study of EEG Signal Classification Using Java Framework

  • Sandeep Kumar Satapathy
  • Alok Kumar Jagadev
  • Satchidananda Dehuri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 309)


With the pace of modern lifestyle, about 40–50 million people in the world suffer from epilepsy—a disease with neurological disorder. Electroencephalography (EEG) is the process of recording brain signals that generate due to a small amount of electric discharge in brain. This may occur due to the information flow among several neurons. Therefore, in every minute, analysis of EEG signal can solve much neurological disorders like epilepsy. In this paper, a systematic procedure for analysis and classification of EEG signal is discussed for identification of epilepsy in a human brain. The analysis of EEG signal is made through a series of steps from feature extraction to classification. Feature extraction from EEG signal is done through discrete wavelet transform (DWT), and the classification task is carried out by MLPNN based on supervised training algorithms such as backpropagation, resilient propagation (RPROP), and Manhattan update rule. Experimental study in a Java platform confirms that RPROP trained MLPNN to classify EEG signal is promising as compared to back-propagation or Manhattan update rule trained MLPNN.


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Copyright information

© Springer India 2015

Authors and Affiliations

  • Sandeep Kumar Satapathy
    • 1
  • Alok Kumar Jagadev
    • 1
  • Satchidananda Dehuri
    • 2
  1. 1.Department of Computer Science and Engineering, Institute of Technical Education and ResearchSOA UniversityBhubaneswarIndia
  2. 2.Department of Systems EngineeringAjou UniversityWoncheon-DongSouth Korea

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