Improving the Feature Stability and Classification Performance of Bimodal Brain and Heart Biometrics
Electrical activities from brain (electroencephalogram, EEG) and heart (electrocardiogram, ECG) have been proposed as biometric modalities but the combined use of these signals appear not to have been studied thoroughly. Also, the feature stability of these signals has been a limiting factor for biometric usage. This paper presents results from a pilot study that reveal the combined use of brain and heart modalities provide improved classification performance and furthermore, an improvement in the stability of the features over time through the use of binaural brain entrainment. The classification rate was increased, for the case of the neural network classifier from 92.4% to 95.1% and for the case of LDA, from 98.6% to 99.8%. The average standard deviation with binaural brain entrainment using all the inter-session features (from all the subjects) was 1.09, as compared to 1.26 without entrainment. This result suggests the improved stability of both the EEG and ECG features over time and hence resulting in higher classification performance. Overall, the results indicate that combining ECG and EEG gives improved classification performance and that through the use of binaural brain entrainment, both the ECG and EEG features are more stable over time.
Unable to display preview. Download preview PDF.
- 5.Palaniappan, R., Krishnan, S.M.: Identifying individuals using ECG signals. In: Proceedings of International Conference on Signal Processing and Communications, Bangalore, India, pp. 569–572 (2004)Google Scholar
- 7.Boulgouris, N.V., Plataniotis, K.N., Micheli-Tzanakou, E. (eds.): Biometrics: Theory, Methods, and Applications. IEEE Press/Wiley, USA (2010)Google Scholar
- 12.Riera, A., Dunne, S., Cester, I., Ruffini, G.: Starfast: a wireless wearable EEG/ECG biometric system based on the enobio sensor. In: Proceedings of the International Workshop on Wearable Micro and Nanosystems for Personalised Health, Valencia, Spain, pp. 21–23, May 2008Google Scholar
- 13.Abdullah, M.K., Subari, K.S., Loong, J.L.C., Ahmad, N.N.: Analysis of the EEG signal for a practical biometric system. World Academy of Science, Engineering and Technology 4(8), 931–935 (2010)Google Scholar
- 14.http://www.biosemi.com/ (accessed June 20, 2012)
- 15.Jasper, H.: The ten twenty electrode system of the international federation. Electroencephalographic and Clinical Neurophysiology 10, 371–375 (1958)Google Scholar
- 16.O’Kelly, J., Magee, W., James, L., Palaniappan, R., Taborin, J., Fachner, J.: Neurophysiological and behavioural responses to music therapy in vegetative and minimally conscious states. Frontiers in Neuroscience - Special Edition on Music, Brain, and Rehabilitation: Emerging Therapeutic Applications and Potential Neural Mechanisms 7(00884) (2013)Google Scholar
- 17.http://www.etymotic.com/ (accessed June 20, 2012)
- 19.Kaul, P., Passafiume, J., Sargent, R.C., O’Hara, B.F.: Meditation acutely improves psychomotor vigilance, and may decrease sleep need. Behavioral and Brain Functions 6(47) (2010). doi:10.1186/1744-9081-6-47
- 20.Shiavi, R.: Introduction to Applied Statistical Signal Analysis, 2nd edn. Academic Press, San Diego (1999)Google Scholar
- 21.Burnham, K.P., Anderson, D.R.: Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd edn. Springer-Verlag, New York (2002)Google Scholar
- 23.Nakanishi, I., Baba, S., Miyamoto, C.: EEG based biometric authentication using new spectral features. In: Proceedings of International Symposium on Intelligent Signal Processing and Communication Systems, Kanazawa, Japan, pp. 651–654 (2009)Google Scholar
- 24.Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Ruspini, H. (ed.) Proceedings of the IEEE International Conference on Neural Networks (ICNN), San Francisco, pp. 586–591 (1993)Google Scholar