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Boostani, R., Graimann, B., Moradi, M.H. and Plurf- scheller, G. (2007) Comparison approach toward finding the best feature and classifier in cue BCI. Journal of Medical and Biological Engineering and Computing, 45, 403-413.

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• TITLE: Performance comparison of neural network training methods based on wavelet packet transform for classification of five mental tasks

  AUTHORS: Vijay Khare, Jayashree Santhosh, Sneh Anand, Manvir Bhatia

  KEYWORDS: Electroencephalogram (EEG); Wavelet Packet Transform (WPT); Artificial Neural Network (ANN)

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.3 No.6, June 25, 2010

  ABSTRACT: In this study, performances comparison to discriminate five mental states of five artificial neural network (ANN) training methods were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw electroencephalogram (EEG) signals. The five ANN training methods used were (a) Gradient Descent Back Propagation (b) Levenberg-Marquardt (c) Resilient Back Propagation (d) Conjugate Learning Gradient Back Propagation and (e) Gradient Descent Back Propagation with movementum.