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Using Brain Waves as New Biometric Feature for Authenticating a Computer User in Real-Time
Kusuma Mohanchandra, Lingaraju G M, Prashanth Kambli, Vinay Krishnamurthy
Pages - 49 - 57     |    Revised - 15-05-2013     |    Published - 30-06-2013
Volume - 7   Issue - 1    |    Publication Date - June 2013  Table of Contents
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KEYWORDS
Cognitive Biometrics, Authentication, Brain Computer Interface, Electroencephalogram, Power Spectral Density.
ABSTRACT
In this paper we propose an Electroencephalogram based Brain Computer Interface as a new modality for Person Authentication and develop a screen lock application that will lock and unlock the computer screen at the users will. The brain waves of the person, recorded in real time are used as password to unlock the screen. Data fusion from 14 sensors of the Emotiv headset is done to enhance the signal features. The power spectral density of the intermingle signals is computed. The channel spectral power in the frequency band of alpha, beta and gamma is used in the classification task. A two stage checking is done to authenticate the user. A proximity value of 0.78 and above is considered a good match. The percentage of accuracy in classification is found to be good. The essence of this work is that the authentication is done in real time based on the meditation task and no external stimulus is used.
CITED BY (5)  
1 Mohanchandra, K., Saha, S., & Lingaraju, G. M. (2015). EEG Based Brain Computer Interface for Speech Communication: Principles and Applications. In Brain-Computer Interfaces (pp. 273-293). Springer International Publishing.
2 Al-Hudhud, G., Alarfag, E., Alkahtani, S., Alaskar, A., Almashari, B., & Almashari, H. (2015, February). Web-based multimodal biometric authentication application. In Information Technology: Towards New Smart World (NSITNSW), 2015 5th National Symposium on (pp. 1-6). IEEE.
3 Kambli, P., & Lingaraju, G. M. Robot Control using Brain Waves.
4 Mishra, P., & Singla, S. K. (2014). Electroencephalogram based biometric framework using time and frequency domain features. Journal of Medical Imaging and Health Informatics, 4(4), 593-599.
5 Del Pozo-Banos, M., Alonso, J. B., Ticay-Rivas, J. R., & Travieso, C. M. (2014). Electroencephalogram subject identification: A review. Expert Systems with Applications, 41(15), 6537-6554.
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Associate Professor Kusuma Mohanchandra
Dayananda Sagar College of Engineering - India
kusumalak@gmail.com
Dr. Lingaraju G M
M S Ramaiah Institute of Engineering - India
Mr. Prashanth Kambli
Assistant Professor/Department of Information Science & Engineering M S Ramaiah Institute of Technology Bangalore, 560054, India - India
Mr. Vinay Krishnamurthy
Student, Department of Computer Science Stony Brook University Stony Brook - 11790, NY, USA - United States of America