The global expansion of biometric systems promotes the emergence of new and more robust biometric modalities. In that context, electroencephalogram (EEG) based biometric interest has been growing in recent years. In this study, a novel approach for EEG representation, based on deep learning, is proposed. The method was evaluated on a database containing 109 subjects, and all 64 EEG channels were used as input to a Deep Convolution Neural Network. Data augmentation techniques are explored to train the deep network and results showed that the method is a promising path to represent brain signals, overcoming baseline methods published in the literature.
CITATION STYLE
Schons, T., Moreira, G. J. P., Silva, P. H. L., Coelho, V. N., & Luz, E. J. S. (2018). Convolutional network for EEG-based biometric. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10657 LNCS, pp. 601–608). Springer Verlag. https://doi.org/10.1007/978-3-319-75193-1_72
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