Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Analysis of the EEG Signal for a Practical Biometric System

Authors: Muhammad Kamil Abdullah, Khazaimatol S Subari, Justin Leo Cheang Loong, Nurul Nadia Ahmad


This paper discusses the effectiveness of the EEG signal for human identification using four or less of channels of two different types of EEG recordings. Studies have shown that the EEG signal has biometric potential because signal varies from person to person and impossible to replicate and steal. Data were collected from 10 male subjects while resting with eyes open and eyes closed in 5 separate sessions conducted over a course of two weeks. Features were extracted using the wavelet packet decomposition and analyzed to obtain the feature vectors. Subsequently, the neural networks algorithm was used to classify the feature vectors. Results show that, whether or not the subjects- eyes were open are insignificant for a 4– channel biometrics system with a classification rate of 81%. However, for a 2–channel system, the P4 channel should not be included if data is acquired with the subjects- eyes open. It was observed that for 2– channel system using only the C3 and C4 channels, a classification rate of 71% was achieved.

Keywords: Biometric, EEG, Wavelet Packet Decomposition, NeuralNetworks

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2606


[1] C. W. Anderson, S. V. Devulapalli, and E. A. Stolz. Determining mental state from EEG signals using parallel implementations of neural networks. Scientific Programming, 4(3):171-183, 1995.
[2] X. Bao, J. Wang, and J. Hu. Method of individual identification based on electroencephalogram analysis. International Conference on New Trends in Information and Service Science, pages 390-393, 2009.
[3] I. Daubechies. Ten Lectures On Wavelets. Society for Industrial and Applied Mathematics Philadelphia, PA, 1992.
[4] C. Gope, N. Kehtarnavaz, and D. Nair. Neural network classification of EEG signals using time-frequency representation. In IEEE International Joint Conference on Neural Networks, volume 4, pages 2502 -2507, Montreal, Canada, 2005.
[5] C. Hema, M. Paulraj, and H. Kaur. Brain signatures: A modality for biometric authentication. In International Conference on Electronic Design, pages 1- 4, Penang, Malaysia, 2008.
[6] P. Jahankhani, V. Kodogiannis, and K. Revett. EEG signal classification using wavelet feature extraction and neural networks. In IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, pages 120 -124, Sofia, Bulgaria, 2006.
[7] H. Jian-Feng. Multifeature biometric system based on EEG signals. In Proceedings of the 2nd International Conference on Interaction Sciences, pages 1341-1345, Seoul, Korea, 2009.
[8] S. Marcel and J. Millan. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):743 -752, 2007.
[9] S. Minfen and S. Fenglin. The analysis of dynamic EEG signals by using wavelet packets decomposition. pages 85 - 88, 1998.
[10] R. Palaniappan and D. P. Mandic. Biometrics from brain electrical activity: A machine learning approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29:738-742, 2007.
[11] R. Palaniappan and D. P. Mandic. EEG based biometric framework for automatic identity verification. Journal of VLSI Signal Processing Systems, 49(2):243-250, 2007.
[12] R. Palaniappan and K. Ravi. Improving visual evoked potential feature classification for person recognition using PCA and normalization. Pattern Recognition Letters, 27(7):726 - 733, 2006.
[13] R. Paranjape, J. Mahovsky, L. Benedicenti, and Z. Koles-. The electroencephalogram as a biometric. In Canadian Conference on Electrical and Computer Engineering, volume 2, pages 1363 -1366, 2001.
[14] M. Poulos, M. Rangoussi, V. Chrissikopoulos, and A. Evangelou. Parametric person identification from the EEG using computational geometry. volume 2, pages 1005 -1008, Pafos, Cyprus, 1999.
[15] M. Poulos, M. Rangoussi, V. Chrissikopoulos, and A. Evangelou. Person identification based on parametric processing of the EEG. volume 1, pages 283 -286, Pafos, Cyprus, 1999.
[16] K. Ravi and R. Palaniappan. Leave-one-out authentication of persons using 40Hz EEG oscillations. In The International Conference on Computer as a Tool, volume 2, pages 1386 -1389, Belgrade, Serbia & Montenegro, 2005.
[17] A. Riera, A. Soria-Frisch, M. Caparrini, C. Grau, and G. Ruffini. Unobtrusive biometric system based on electroencephalogram analysis. EURASIP Journal on Advances in Signal Processing, 2008:18, 2008.
[18] G. Singhal and P. RamKumar. Person identification using evoked potentials and peak matching. In Biometrics Symposium, pages 1- 6, Baltimore, MD, 2007.
[19] V. Srinivasan, C. Eswaran, and N. Sriraam. Approximate entropybased epileptic EEG detection using artificial neural networks. IEEE Transactions on Information Technology in Biomedicine, 11(3):288-295, 2007.
[20] A. Subasi, A. Alkan, E. Koklukaya, and M. K. Kiymik. Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing. Neural Networks, 18(7):985-997, 2005.
[21] L. Sun and M. Shen. Analysis of non-stationary electroencephalogram using the wavelet transformation. In 6th International Conference on Signal Processing, volume 2, pages 1520 - 1523, Beijing, China, 2002.
[22] S. Sun. Multitask learning for EEG-based biometrics. In International Conference on Pattern Recognition, pages 1- 4, Tampa, FL, 2008.
[23] P. Tangkraingkij, C. Lursinsap, S. Sanguansintukul, and T. Desudchit. Selecting relevant EEG signal locations for personal identification problem using ICA and neural network. In 8th IEEE/ACIS International Conference on Computer and Information Science, pages 616 - 621, Shanghai, China, 2009.
[24] W. Ting, Y. Guo-zheng, Y. Bang-hua, and S. Hong. EEG feature extraction based on wavelet packet decomposition for brain computer interface. Measurement, 41(6):618 - 625, 2008.
[25] A. Yazdani, A. Roodaki, S. Rezatofighi, K. Misaghian, and S. Setarehdan. Fisher linear discriminant based person identification using visual evoked potentials. In 9th International Conference on Signal Processing, pages 1677 -1680, Beijing, China, 2008.