Deep Learning for Mind Wave Electroencephalographic Biometric Security

Authors

  • Nagsen S Bansod  MGM'S Dr. G. Y. Pathrikar College of Computer Science and Inforation Technology, Aurangabad, India
  • Siddharth Dabahade  MGM'S Dr. G. Y. Pathrikar College of Computer Science and Inforation Technology, Aurangabad, India
  • M M Kazi  MGM'S Dr. G. Y. Pathrikar College of Computer Science and Inforation Technology, Aurangabad, India
  • Jitendra Dongre  Psychiatry Department, Byramjee Jeejeebhoy Government Medical Collegeand Sassoon General Hospitals, Pune, India
  • Prapti Deshmukh  MGM'S Dr. G. Y. Pathrikar College of Computer Science and Inforation Technology, Aurangabad, India
  • K V Kale  Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India

Keywords:

EEG, Mindwave, Identification, Verification, Biometric

Abstract

Brain generates various signals according to the situation and activates. The frequency of the brain is different as per the level of action taken place by the person it may be either imaginary or motor imagery activities. From the brain signals imaginary signals are captured using MindWave Mobile Portable device. Frequency wise channels are separated and categories as Delta, Theta, Alpha and Beta. These channels are indicated emotions, movement, sensations, vision, etc. Features are extracted of each channel using Power Spectral Density (PSD) function. Feature level fusion is used for pattern matching. The Novelty of this work is a single electrode device is used to capture an Electroencephalography (EEG) imaginary data & feature level fusion of channels. The results are proven that these EEG imaginary signals could be used as better biometrics based authentication system.

References

  1. H. A. Shedeed, "A new method for person identification in a biometric security system based on brain EEG signal processing," Information and Communication Technologies (WICT), 2011 World Congress on, Mumbai, 2011, pp. 1205-1210.2.
  2. Y. H. Yu, P. C. Lai, L. W. Ko, C. H. Chuang, B. C. Kuo and C. T. Lin, "An EEG-based classification system of Passenger's motion sickness level by using feature extraction/selection technologies," The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, 2010, pp. 1-6. doi: 10.1109/IJCNN.2010.5596739.
  3. S. Yang and F. Deravi, "Novel HHT-Based Features for Biometric Identification Using EEG Signals," Pattern Recognition (ICPR), 2014 22nd International Conference on, Stockholm, 2014, pp. 1922-1927.
  4. J. f. Hu, "Biometric System Based on EEG Signals: A Nonlinear Model Approach," Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on, Kaifeng, China, 2010, pp. 48-51.
  5. H. Jian-feng, "Comparison of Different Classifiers for Biometric System Based on EEG Signals," Information Technology and Computer Science (ITCS), 2010 Second
  6. M. Garau, M. Fraschini, L. Didaci and G. L. Marcialis, "Experimental results on multi-modal fusion of EEG-based personal verification algorithms," 2016 International Conference on Biometrics (ICB), Halmstad, 2016, pp. 1-6. doi: 10.1109/ICB.2016.7550080
  7. M. Abo-Zahhad, S. M. Ahmed and S. N. Abbas, "State-of-the-art methods and future perspectives for personal recognition based on electroencephalogram signals," in IET Biometrics, vol. 4, no. 3, pp. 179-190, 9 2015.
  8. doi: 10.1049/iet-bmt.2014.0040
  9. M. Fraschini, A. Hillebrand, M. Demuru, L. Didaci and G. L. Marcialis, "An EEG-Based Biometric System Using Eigenvector Centrality in Resting State Brain Networks," in IEEE Signal Processing Letters, vol. 22, no. 6, pp. 666-670, June 2015. doi: 10.1109/LSP.2014.2367091
  10. M. V. Ruiz Blondet, S. Laszlo and Z. Jin, "Assessment of permanence of non-volitional EEG brainwaves as a biometric," Identity, Security and Behavior Analysis (ISBA), 2015 IEEE International Conference on, Hong Kong, 2015, pp. 1-6. doi: 10.1109/ISBA.2015.7126359
  11. B. Singh, S. Mishra and U. S. Tiwary, "EEG based biometric identification with reduced number of channels," 2015 17th International Conference on Advanced Communication Technology (ICACT), Seoul, 2015, pp. 687-691.doi: 10.1109/ICACT.2015.7224883
  12. Mashail Alsolamy, Anas Fattouh, "Emotion estimation from EEG signals during listening to Quran using PSD features", 2016 7th International Conference on Computer Science and Information Technology (CSIT), vol. 00, no. , pp. 1-5, 2016, doi:10.1109/CSIT.2016.7549457
  13. L. Ma, J. W. Minett, T. Blu and W. S. Y. Wang, "Resting State EEG-based biometrics for individual identification using convolutional neural networks," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015, pp. 2848-2851.doi: 10.1109/EMBC.2015.7318985
  14. N. S. Bansod, S. B. Dabhade, M. M. Kazi, Y. S. Rode and K. V. Kale, "Single electrode brain signal data fusion for security," 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, 2016, pp. 108-112.doi: 10.1109/ICGTSPICC.2016.7955279
  15. N. S. Bansod, S. B. Dadhade, S. S. Kawathekar and K. V. Kale, "Speaker Recognition Using Marathi (Varhadi) Language," 2014 International Conference on Intelligent Computing Applications, Coimbatore, 2014, pp. 421-425.doi: 10.1109/ICICA.2014.92
  16. S. B. Dabhade, N. S. Bansod, Y. S. Rode, M. M. Kazi and K. V. Kale, "Hyper spectral face image based biometric recognition," 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, 2016, pp. 559-561.doi: 10.1109/ICGTSPICC.2016.7955363
  17. S. B. Dabhade, N. S. Bansod, Y. S. Rode, M. M. Kazi and K. V. Kale, "Multi sensor, multi algorithm based face recognition & performance evaluation," 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, 2016, pp. 113-118.doi: 10.1109/ICGTSPICC.2016.7955280

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-03-11

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Volume 4, Issue 4, March-April-2019

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Research Articles

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How to Cite

[1]
Nagsen S Bansod, Siddharth Dabahade, M M Kazi, Jitendra Dongre, Prapti Deshmukh, K V Kale, " Deep Learning for Mind Wave Electroencephalographic Biometric Security , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 4, pp.89-96, March-April-2019.