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Individual Feature Extraction and Identification on EEG Signals in Relax and Visual Evoked Tasks

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Biomedical Informatics and Technology (ACBIT 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 404))

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Abstract

Compared to conventional biometrics, electroencephalogram (EEG) signal has obvious advantages in uniqueness, high confidentiality and impossibility to steal or mimic. In this paper, we investigated EEG signals in relax task and visual evoked task and compared their potentials as the biometric authentication feature. 20 subjects were recruited, and each performed two tasks while 64-channel EEG signals were recorded continuously. The extracted features, autoregression (AR) model, power spectrum of the time-domain (TPS), power spectrum of the frequency-domain (FPS) and phase-locking value (PLV), were given to a support vector machine (SVM) for classification respectively. The results showed that visual evoked task presented better performance in identifying the individuals than the relax task did. Specially, among all these features, AR model got the highest accuracy in both tasks, achieving 90.53% and 96.25% respectively for relax task and visual evoked task. Then support vector machine-recursive feature elimination (SVM-RFE) was employed to select the most discriminative channels just for AR model based on VEP signals for it showed the best performance. Additionally, it gave a higher accuracy of 97.25% based on the 32 top ranked channels. Further investigation may help develop an alternative EEG based biometric system to enhance the traditional biometric technologies.

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Authors and Affiliations

  1. Neural Engineering & Rehabilitation Lab, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, P.R. China

    Shuang Liu, Yanru Bai, Jing Liu, Hongzhi Qi, Penghai Li, Xin Zhao, Peng Zhou, Lixin Zhang, Baikun Wan, Chunhui Wang, Shanguang Chen & Dong Ming

  2. National Laboratory of Human Factors Engineering, China Astronaut Research and Training Centre, Beijing, 100193, China

    Chunhui Wang, Qijie Li & Xuejun Jiao

Authors
  1. Shuang Liu
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  2. Yanru Bai
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  3. Jing Liu
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  4. Hongzhi Qi
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  5. Penghai Li
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  6. Xin Zhao
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  7. Peng Zhou
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  8. Lixin Zhang
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  9. Baikun Wan
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  10. Chunhui Wang
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  11. Qijie Li
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  12. Xuejun Jiao
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  13. Shanguang Chen
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  14. Dong Ming
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Editor information

Editors and Affiliations

  1. Aizu Research Cluster for Medical Engineering and Informatics Research Center for Advanced Information Science and Technology, The University of Aizu, 965-8580, Aizuwakamatsu, Fukushima, Japan

    Tuan D. Pham

  2. Department of Cancer Biology, Division of Mathematical Oncology, The Institute of Medical Science, The University of Tokyo, 108-8639, Tokyo, Japan

    Kazuhisha Ichikawa

  3. Chaos Technology Research Lab, 5-26-5 Seta, 520-2134, Otsu-shi, Oyama-Higa, Japan

    Mayumi Oyama-Higa

  4. Department of Biostatistics and Medical Informatics, Faculty of Medicine and Pharmacy, The Free University of Brussels, 103, 1090, Laarbeeklaan, Brussels, Belgium

    Danny Coomans

  5. Department of Computer Science, University of Muenster, Einsteinstrasse 62, 48149, Muenster, Germany

    Xiaoyi Jiang

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Liu, S. et al. (2014). Individual Feature Extraction and Identification on EEG Signals in Relax and Visual Evoked Tasks. In: Pham, T.D., Ichikawa, K., Oyama-Higa, M., Coomans, D., Jiang, X. (eds) Biomedical Informatics and Technology. ACBIT 2013. Communications in Computer and Information Science, vol 404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54121-6_29

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  • DOI: https://doi.org/10.1007/978-3-642-54121-6_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54120-9

  • Online ISBN: 978-3-642-54121-6

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