Non-negative matrix factorizations based spontaneous electroencephalographic signals classification using back propagation feedback neural networks

  • Authors:

  • Mingyu Liu;Jue Wang;Chongxun Zheng

  • Affiliations:

  • The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an, Shannxi, China;The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an, Shannxi, China;The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an, Shannxi, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

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Abstract

The paper proposes a new spontaneous EEG classification method for attention-related tasks. The algorithm was based on back propagation feedback neural network. Non-Negative Matrix Factorization (NMF) was used as a feature extraction tool. Six electrodes were selected from 32 international 10-20 electrode placement systems according to surface power distributing of EEG activity. Several experiments were carried out to decide an adaptive and robust structure of BP-ANN. The final structure of the NMF-ANN preserved the spatio-temporal characteristics of the signal. Simulation results showed that the averaged classification accuracy for designed three-level tasks can reach 98.4%, 86%, and 82.8%, which were better than other two reference methods.