การใช้ตำแหน่ง 2 ตำแหน่งในการพิสูจน์ตัวตนโดยใช้สัญญาณคลื่นสมองช่วงเดลต้า
Main Article Content
บทคัดย่อ
- งานวิจัยนี้ได้ศึกษาถึงเรื่องการพิสูจน์ตัวตนโดยใช้คลื่นสมองช่วงเดลต้ามาศึกษา มีจุดประสงค์เพื่อที่จะใช้ตำแหน่งเพียง 2 ตำแหน่งของคลื่นสมองในการพิสูจน์ตัวตน จากหลักการของโครงข่ายประสาทแบบมีการสอน (supervised neural network) จำนวนคุณสมบัติที่น้อยลง ทำให้การเรียนรู้มีประสิทธิภาพดียิ่งขึ้น ดังนั้นวัตถุประสงค์ของงานวิจัยนี้คือการศึกษาประสิทธิภาพการใช้ตำแหน่งคลื่นสมองในการพิสูจน์ตัวตน 2 ตำแหน่ง โดยใช้คลื่นสมองช่วงเดลต้าของผู้ทดลอง 40 คน มีการใช้เทคนิคการวิเคราะห์องค์ประกอบอิสระ (ICA) โดยวิธี SOBIRO ในการแยกสัญญาณรบกวนออกจากสัญญาณคลื่นสมองของแต่ละบุคคลและคัดแยกคลื่นสมองโดยใช้ช่วงที่มีความถี่ต่ำกว่า 4 เฮิรตซ์มาทดสอบ ใช้เทคนิคของโครงข่ายประสาทเทียมในการพิสูจน์ตัวตนของบุคคล 40 คน โดยมีการเปลี่ยนค่าจำนวนเซลล์ประสาทในชั้นข้อมูลแอบแฝง (Hidden layer) ตั้งแต่ 5-26 เซลล์เพื่อหาค่าที่เหมาะสม ในการหาตำแหน่ง 2 ตำแหน่งในการพิสูจน์ตัวตน
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.
เอกสารอ้างอิง
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.
