Two Positions for Personal Authentication Using The Delta Brain Wave Signal
Article Sidebar
Main Article Content
Abstract
- This study discusses the authentication that uses delta brainwave signals. The purpose is to use only two positions of brainwaves to prove authentication. Based on the principle of supervised neural network, the number of features is reduced. Make learning more effective. The objective of this study was to investigate two-position of brainwave. The Delta brainwave signals of 40 subjects are explored. The practical technique, Independent Component Analysis (ICA) by SOBIRO algorithm is considered clean and separates the individual signals from noise. Delta brainwaves are extracted from brain signal for group recognition using the technique of supervised neural network for authenticating 40 subjects. The number of neurons in the hidden layer 5-26 neurals were test to find the optimal value of authentication for two positions brainwaves
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
I/we certify that I/we have participated sufficiently in the intellectual content, conception and design of this work or the analysis and interpretation of the data (when applicable), as well as the writing of the manuscript, to take public responsibility for it and have agreed to have my/our name listed as a contributor. I/we believe the manuscript represents valid work. Neither this manuscript nor one with substantially similar content under my/our authorship has been published or is being considered for publication elsewhere, except as described in the covering letter. I/we certify that all the data collected during the study is presented in this manuscript and no data from the study has been or will be published separately. I/we attest that, if requested by the editors, I/we will provide the data/information or will cooperate fully in obtaining and providing the data/information on which the manuscript is based, for examination by the editors or their assignees. Financial interests, direct or indirect, that exist or may be perceived to exist for individual contributors in connection with the content of this paper have been disclosed in the cover letter. Sources of outside support of the project are named in the cover letter.
I/We hereby transfer(s), assign(s), or otherwise convey(s) all copyright ownership, including any and all rights incidental thereto, exclusively to the Journal, in the event that such work is published by the Journal. The Journal shall own the work, including 1) copyright; 2) the right to grant permission to republish the article in whole or in part, with or without fee; 3) the right to produce preprints or reprints and translate into languages other than English for sale or free distribution; and 4) the right to republish the work in a collection of articles in any other mechanical or electronic format.
We give the rights to the corresponding author to make necessary changes as per the request of the journal, do the rest of the correspondence on our behalf and he/she will act as the guarantor for the manuscript on our behalf.
All persons who have made substantial contributions to the work reported in the manuscript, but who are not contributors, are named in the Acknowledgment and have given me/us their written permission to be named. If I/we do not include an Acknowledgment that means I/we have not received substantial contributions from non-contributors and no contributor has been omitted.
References
2. Paranjape RB, Mahovsky J, Benedicenti L, Koles Z. “The electroencephalogram as a biometrics.” Proc Can Conf Electr. ComputEng 2, pp.1363–1366, 2001.
3. Poulos M, Rangoussi M, Alexandris N, Evangelou. “A On the use of EEG features towards person identification via neural networks.” Med Inform Internet Med 26(1), pp.35–48, 2001.
4. Poulos M, Rangoussi M, Alexandris N, Evangelou A. “Person identification from the EEG using nonlinear signal classification.” Methods Inf Med 41(1), pp.64–75, 2002.
5. Palaniappan R, Ravi KVR. “A new method to identify individuals using signals from the brain.” Proceedings of fourth international conference information communication and signal processing, pp 15–18, 2003.
6. Palaniappan R, Mandic D.P. “Biometrics from brain electrical activity: a machine learning approach.” IEEE Trans Pattern Anal Mach Intell 29, pp.738–742, 2007.
7. Palaniappan R. “Method of identifying individuals using VEP signals and neural network.” IEEE Proc Sci Mea Technol 151(1), pp.16–20, 2004.
8. Palaniappan R, Mandic D.P., EEG based biometric framework for automatic identity verification. VLSI Signal Process 2(2), pp.243–250, 2007.
9. Marcel S, Millan J. “Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation.” IEEE Trans Pattern Anal Mach Intell 29(4), pp.743–752, 2007.
10. Tangkraingkij P, Lursinsap C, Sanguansintukul S, Desudchit.T. “Selecting relevant EEG signal locations for personal identification problem using ICA and neural network.” Eighth IEEE/ACIS international conference on computer and information science (ICIS 2009), 2009, pp.616–621.
11. Tangkraingkij P, Lursinsap C, Sanguansintukul S, Desudchit T.”Personal identification by EEG using ICA and neural network.” Computational science and its applications (ICCSA2010), Lecture Notes in Computer Science vol 6018, 2010. pp 419–430.
12. Tangkraingkij P, Lursinsap C, Sanguansintukul S, Desudchit T. ”Insider and outsider person authentication with minimum number of brain wave signals by neural and homogeneous identity filtering.” Neural Computing & Applications, Volume 22, Issue 1 Supplement, pp. 463-476, 2013.
[13] Tangkraingkij P. “Significant Frequency Range of Brainwave Signals for Authentication.” Study in Computer Intelligence 612 (Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2015), 2015, pp.103-113.
14. Boger, Z., and Guterman, H. “Knowledge extraction from artificial neural network models” IEEE Systems, Man, and Cybernetics Conference, Orlando, FL, USA. 1997.
15. Berry, M.J.A., and Linoff, G. Data Mining Techniques, NY: John Wiley & Sons. 1997.
16. Blum, A. Neural Networks in C++, NY: Wiley. 1992.
17. T. Preecha, M. Ajjima, N. Isara. 2017. “An Appropriate Number of Neurons in a Hidden Layer for Personal Authentication Using Delta Brainwave Signals” 2nd International Conference on Control and Robotics Engineering, 2017, pp. 232 -236.
18. Cichocki, A.”Blind Signal Processing Methods for Analyzing Multichannel Brain Signals.” International Journal of Bioelectromagnetism 6. (1). 2004.
19. Cichocki, A., Amari, S.,Siwek, K., Tanaka T., et al.: ICALAB toolboxes. Available online at https://www.bsp.brain.riken.jp/ICALAB. [Accessed Jan. 15, 2018].
20. C. Kaewwit, C. Lursinsap, P. Sophatsathit. High accuracy EEG biometrics identification using ICA And AR Model, Journal Of Ict, 16, 2: 354-373, 2017.
21. Wu Q, Zeng Y, Zhang C, Tong L, Yan B. An EEG-based person authentication system with open setcapability combining eye blinking signals. Sensors2018, 18, 335, 2018.