EEG-based biometric identification with convolutional neural network

  • J. X. ChenEmail author
  • Z. J. Mao
  • W. X. Yao
  • Y. F. Huang


Although more interest arising in biometric identification with electroencephalogram (EEG) signals, there is still a lack of simple and robust models that can be applied in real applications. This work proposes a new convolutional neural network with global spatial and local temporal filter called (GSLT-CNN), which works directly with raw EEG data, not requiring the need for engineering features. We investigate the performance of the GSLT-CNN model on datasets of 157 subjects collected from 4 different experiments that measure endogenous brain states (driving fatigue and emotion) as well as time-locked artificially induced brain responses such as rapid serial visual response (RSVP). We evaluate the GSLT-CNN model against the comparable SVM, Bagging Tree and LDA models with effective feature selection method. The results show the GSLT-CNN model is highly efficient and robust in training more than 279 K epochs within less than 0.5 h and achieves 96% accuracy in identifying 157 subjects, which is 3% better than the best accuracy of SVM on selected PSD feature, 10% better than that of SVM on selected AR feature and 23% better than that of normal CV-CNN model on raw EEG feature. It demonstrates the potential of deep learning solutions for real-life EEG-based biometric identification. We also show that the cross-session identification accuracy from time-locked RSVP data (99%) is slightly higher than that from single-session non-time-locked driving fatigue data (97%) and much higher than that from epochs measuring random brain states (90%), which implies RSVP could be a more beneficial design to achieve high identification accuracy with EEG and our GSLT-CNN model is robust for cross-session identification in RSVP experiment.


Biometric identification Electroencephalogram (EEG) Convolutional neural networks Deconvolutional networks Brain-computer interface 



This work was supported by the National Natural Science Foundation of China Projects under the Project Agreement Number 61806118 and 61806144.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Electronical and Information EngineeringShaanxi University of Science and TechnologyXi’anChina
  2. 2.Department of Electrical and Computer EngineeringUniversity of Texas at San AntonioSan AntonioUSA
  3. 3.Department of KinesiologyUniversity of Texas at San AntonioSan AntonioUSA
  4. 4.Department of Epidemiology and BiostatisticsUniversity of Texas Health Science Center at San AntonioSan AntonioUSA

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