Feature Extraction and Performance Analysis of EEG Signal Using S-Transform

  • Monorama Swain
  • Rutuparna Panda
  • Himansu Mahapatra
  • Sneha Tibrewal
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)


Feature can be described as a functional component observed from a data set. The extracted features give the information related to a signal, thus it requires to calculate cost of information processing and complexity of analyzing a huge data set. This paper presents a feature extraction method using S transform. Five data sets are taken and feature extraction has been performed by implementing two methods: first by applying S-transform and other without S-transform. The performance of the neural model is evaluated on the basis of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.


Feature abstraction EEG signal Neural network S-transform 


  1. 1.
    Subasi, A.: EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl. 32, 1084–1093 (2007)Google Scholar
  2. 2.
    Adeli, H., Zhou, Z., Dadmehr, N.: Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods. 123, 69–87 (2003)CrossRefGoogle Scholar
  3. 3.
    Jahankhani, P., Kodogiannis, V., Revett, K.: EEG signal classification using wavelet feature extraction and neural networks. International Symposium on Modern Computing, pp. 52–57 (2006)Google Scholar
  4. 4.
    Subasi, A.: Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients. Expert Syst. Appl. 28, 701–711 (2005)CrossRefGoogle Scholar
  5. 5.
    Subasi, A., Ercelebi, E.: Classification of EEG signals using neural network and logistic regression. Comput. Methods Programs Biomed. 78, 87–99 (2005)CrossRefGoogle Scholar
  6. 6.
    Petrosian, A., Prokhorov, D., Homan, R., Dashei, R., Wunsch, D.: Recurrent neural network based prediction of epileptic seizures intra and extra cranial EEG. Neurocomputing, 30, 201–218 (2000)Google Scholar
  7. 7.
    Jian-feng, H.U.: Multifeature analysis in motor imagery EEG classification. In: Proceedings of 3rd International Symposium on Electronic Commerce and Security of IEEE, pp. 114–117 (2010)Google Scholar
  8. 8.
    Mishra, S., Bhende, C.N., Panigrahi, B.K.: Detection and classification of power quality disturbances using S-transform and probabilistic neural network. IEEE Trans. Power Deliv. 23(1), 280–287 (2008)Google Scholar
  9. 9.
    Krusienski, D.J., McFarland, D.J., Wolpaw, J.R.: An evaluation of autoregressive spectral estimation model order for brain-computer interface applications. In: EMBS Annual International Conference of IEEE, New York, pp. 1323–1326 (2006)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Monorama Swain
    • 1
  • Rutuparna Panda
    • 2
  • Himansu Mahapatra
    • 1
  • Sneha Tibrewal
    • 1
  1. 1.Silicon Institute of TechnologyBhubaneswarIndia
  2. 2.Department of ECEVeer Surendra Sai University of TechnologySambalpurIndia

Personalised recommendations