Abstract
This paper introduces a deep learning approach to the feature extraction of P300 cognitive component existing in electroencephalogram signals collected in an autobiographical paradigm test. A thorough belief mechanism is used for the extraction of deep characteristics rather than raw feature vectors to train the classifier. It is shown that the classification accuracy is satisfactory by learning deep from the experimental data. Experiments have validated the usefulness of the algorithm. The hidden information has been obtained accurately with a single electroencephalogram channel. Moreover, performances of support vector machine with different feature extraction methods are compared.
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Acknowledgement
Special thanks would be expressed to Dr. V. Abootalebi and the Research Center of Intelligent Signal Processing (RCISP), Iran, for the provision of the data. Besides, Dr. Deng Wang with department of Computer Science and Technology, Tongji University also deserves the appreciation, for the data support.
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Liu, Q., Zhao, XG., Hou, ZG., Liu, HG. (2017). Deep Belief Networks for EEG-Based Concealed Information Test. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_58
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DOI: https://doi.org/10.1007/978-3-319-59081-3_58
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