Analysis of the EEG Signal for a Practical Biometric System
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:50905", author = "Muhammad Kamil Abdullah and Khazaimatol S Subari and Justin Leo Cheang Loong and Nurul Nadia Ahmad", title = "Analysis of the EEG Signal for a Practical Biometric System", abstract = "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", volume = "4", number = "8", pages = "316-5", }
