The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of EEG Reproducibility for Personal Authentication

개인인증을 위한 뇌파의 재현성에 대한 분석

  • Received : 2020.04.17
  • Accepted : 2020.06.15
  • Published : 2020.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we presented the results of analysis through EEG measurement for the purpose of checking the frequency band of EEG signals that can be used for personal authentication. The measurement status was divided into the open-eye state and the closed-eye state depending on the presence or absence of an optical task. The data measured in the EEG experiments was divided into seven frequency bands : delta waves, theta waves, alpha waves, SMR waves, mid-beta waves, beta waves and gamma waves to identify the frequency band with the smallest power fluctuation over time. In our results, there was no significant difference between the open-eye state and the closed-eye state, and the SMR waves and mid-beta waves related to human concentration had the smallest fluctuation in power over time, and were a highly reproducible frequency band.

본 논문에서는 개인인증 시 활용 가능한 EEG 신호의 주파수 대역을 확인하기 위하여 뇌파 측정을 통해 분석한 결과를 제시하였다. 시각 과제의 유무에 따라 개안 상태와 폐안 상태로 구분하여 뇌파를 측정하였으며, 이를 델타파, 세타파, 알파파, SMR파, 중간베타파, 베타파 및 감마파의 7종류의 주파수 대역으로 나누어 시간에 따른 파워의 변동이 가장 작은 주파수 대역을 관찰하였다. 본 논문의 결과에서는 개안 상태와 폐안 상태에서는 유의한 차이가 나타나지 않았으나, 인간의 집중과 관련한 SMR파 및 중간베타파가 시간에 따른 파워의 변동이 가장 작게 관찰되었기에 재현성이 높은 주파수 대역인 것으로 나타났다.

Keywords

References

  1. H. Gurkan, U. Guz, and B. S. Yarman, "A novel biometric authentication approach using electrocardiogram signals," Int. Conf. of the IEEE EMBS, Osaka, Japan, July 2013, pp. 4259-4262.
  2. J. Cho, "Personal authentication performance evaluation of ECG and PCG bio-signals using principal component," Master's Thesis, Chungbuk National University Graduate School of Information Security Management, 2018.
  3. W. Khalifa, A. Salem, M. Roushdy, and K. Revett, "A Survey of EEG Based User Authentication Schemes", The 8th Int. Conf. Informatics and Systems, Giza, Egypt, May 2012, pp. 55-60.
  4. G. Choi, E. Kim, Y. Kang, S. Park, S. Park, S. Choi, and H. Hwang, "Development of a Biometric Authentication System Based on Electroencephalography," J. of Biomedical Engineering Research, vol. 39, no. 1, 2018, pp. 43-47. https://doi.org/10.9718/JBER.2018.39.1.43
  5. J. Hwan, "A Study on Personal Authentication Technology Using Multiple Biometrics method," Doctor's Thesis, Dankook University Graduate School of Electronics and Electrical Engineering, 2019.
  6. Y. Jang and J. Han, "Comparison of EEG Characteristics between Dementia Patient and Normal Person Using Frequency Analysis Method," J. of the Korea Institute of Electronic Communication Sciences, vol. 9, no. 5, May 2014, pp. 589-594. https://doi.org/10.13067/JKIECS.201.9.5.589
  7. Y. Jang, K. Park, and D. Han "EEG Signal Analysis for Relativity between Musical stimulus and Arithmetical Brain Activity," J. of the Korea Institute of Electronic Communication Sciences, vol. 9, no. 5, May 2014, pp. 595-600. https://doi.org/10.13067/JKIECS.201.9.5.595
  8. J. Kim and G. Park, "Personal authentication using biological signals Technology and DB construction," TTA J. of Telecommunications Technology Association, vol. 165, no. 1, May 2016, pp. 41-46.
  9. A. Riera, A. S. Frisch, M. Caparrini, I. Cester, and G. Ruffini, Biometrics: Theory, Methods, and Applications. New Jersey: Wiley, 2009.
  10. S. Sun, "Multitask Learning for EEG-Based Biometrics," Int. Conf. on Pattern Recognition, Florida, USA, Dec. 2008, pp. 51-55.
  11. A. Zuquete, B. Quintela and P. S. Cunha, "Biometric Authentication using Brain Responses to Visual Simuli," Int. Conf. Bio-inspired Systems and Signal Processing, Valencia, Spain, Jan. 2010, pp. 103-110.