Abstract
Personal identification plays an important role in the information society. However, the traditional methods of identification cannot fully guarantee security. As a new type of biometrics, brainprint has remarkable advantages of non-stealing and unforgeability. It is a more secure biometrics for personal identification. In this paper, we propose a new method for brainprint recognition based on brain networks of electroencephalogram (EEG) signals. Firstly, we construct the brain functional networks upon the phase synchronization of EEG channels. Then, the degree of brain networks is computed to form a novel feature vector. Lastly, we utilize linear discriminant analysis (LDA) to classify extracted features. Experiments are conducted on four data sets. The average recognition accuracy of each data set is over 0.937 and the best one reaches 0.993.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (Grant No. 61671193), International Science & Technology Cooperation Program of China (Grant No. 2014DFG12570)
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Kong, W., Fan, Q., Wang, L., Jiang, B., Peng, Y., Zhang, Y. (2017). Task-Free Brainprint Recognition Based on Degree of Brain Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_72
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DOI: https://doi.org/10.1007/978-3-319-70096-0_72
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