In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.
 Teplan, M. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2 (2), 1-11.
 Eischen, S.E., Luckeritz, J.Y., Polish, J. (1995). Spectral analysis of EEG from families. BiologicalPsychology, 41, 61-68.
 Svidevskaya, N.E., Koroľkova, T.A. (1995). Genetic features of the human cerebral cortex. Neuroscience and Behavioural Physiology, 25 (5), 370-376.
 Vogel, F. (1970). The genetic basis of the normal EEG. Human Genetics, 10, 91-114.
 Poulos, M., Rangoussi, M., Hrissikopoulos, V., Evangelou, A. (1999). Person identification based on parametric processing of the EEG. In Proceedings of the IEEE International Conference on Electronics,Circuits, and Systems, 5-8 September 1999. IEEE, 283-286.
 Biel, L., Pettersson, O., Philipson, L., Wide, P. (2001). ECG analysis: A new approach in human identification. IEEE Transactions on Instrumentation and Measurement, 50, 808-812.
 Irvine, J.M., Wiederhold, B.K., Gavshon, L.W., Israel, S., McGehee, S.B., Meyer, R., Wiederhold, M.D. (2001). Heart rate variability: A new biometric for human identification. In International Conference on Artificial Intelligence (IC-AI 2001). Las Vegas, Nevada, 1106-1111.
 Gupta, C.N., Palaniappan, R., Swaminathan, S. (2008). On the analysis of various techniques for a novel Biometric system. International Journal of Medical Engineering and Informatics, 1 (2), 266-273.
 Ursulean, R., Lazar, A.M. (2009). Detrended crosscorrelation analysis of biometric signals used in a new authentication method. Electronics and Electrical Engineering, 1 (89), 55-58.
 Gupta, C.N., Khan, Y.U., Palaniappan, R., Sepulveda, F. (2009). Wavelet framework for improved target detection in oddball paradigms using P300 and gamma band analysis. Biomedical Soft Computing and Human Sciences, 14 (2), 61-67.
 Palaniappan, R., Eswaran, C. (2009). Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance. Applied Soft Computing, 9, 100-106.
 Abdullah, M.K., Subari, K.S., Cheang Loong, J.L., Ahmad, N.N. (2010). Analysis of the EEG signal for a practical biometric system. World Academy of Science,Engineering and Technology, 68, 1123-1127.
 Yang, X., Dai, J., Zhang, H., Wu, B., Su, Y., Chen, W., Zheng, X. (2011). P300 Wave based person identification using LVQ neural network. Journal of Convergence Information Technology, 6 (3), 296-302.
 Gupta, C.N., Palaniappan, R., Paramesran, R. (2012). Exploiting the P300 paradigm for cognitive biometrics. International Journal of Cognitive Biometrics, 1 (1), 26-38.
 Singh, Y.N., Singh, S.K. (2012). Evaluation of electrocardiogram for biometric authentication. Journal of Information Security, 12 (3), 39-48.
 Israel, S.A., Scruggs, W.T., Worck, W.J., Irvine, J.M. (2003). Fusing face and ECG for person identification. In Proceedings of IEEE Applied Imagery Pattern Recognition Workshop, 15-17 October 2003. IEEE, 225-231.
 Shen, T.W., Tompkins, W.J., Hu, Y.H (2002). Onelead ECG for identity verification. In ProceedingsIEEE EMBS/BMES Conference, 23-26 October 2002. IEEE, 62-63.
 Makeig, S., Bell, A.J., Jung, T.P., Sejnowski, T.J. (1996). Independent component analysis of electroencephalographic data. Advances in Neural InformationProcessing Systems, 8, 145-151.
 Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEETransactions on Neural Networks, 10 (3), 626-634.
 Vigario, R., Sarela, J., Jousmaki, V., Hamalainen, M., Oja, E. (2000). Independent component approach to the analysis of EEG and MEG recordings. IEEETransactions on Biomedical Engineering, 47 (5), 589-593.
 Jiang, J.A., Chao, C.F., Chiu, M.J., Lee, R.G., Tseng, C.L., Lin, R. (2007). An automatic analysis method for detecting and eliminating ECG artifacts in EEG. Computers in Biology and Medicine, 37, 1660-1671.
 Gupta, C.N., Palaniappan, R. (2011). Reducing power spectral density of eye blink artifact through improved genetic algorithm. In International Conference onBioinformatics and Biomedical Technology, 25-27 March 2011.
 Palaniappan, R., Gupta, C.N. (2006). Genetic algorithm based independent component analysis to separate noise from electrocardiogram signals. In Proceedings of IEEE International Conference onEngineering of Intelligent Systems, 22-23 April 2006. IEEE, 1-5.
 Hyvarinen, A., Erkki, O. (2000). Independent component analysis: Algorithms and applications. Neural Networks, 13 (4-5), 411-430.
 Ungureanu, M., Bigan, C., Strungaru, R., Lazarescu, V. (2004). Independent component analysis applied in biomedical signal processing. Measurement ScienceReview, 4, 1-8.
 Krishnaveni, V., Jayaraman, S., Kumar, P.M., Shivakumar, K., Ramadoss, K. (2005). Comparison of independent component analysis algorithms for removal of ocular artifacts from electroencephalogram. Measurement Science Review, 5 (2), 67-78.
 Xu, L., Cheung, C., Yang, H., Amari, S. (1997). Independent component analysis by the informationtheoretic approach with mixture of densities. IEEETransactions on Neural Networks, 5, 1821-1826.
 Agrawal, G., Singh, M., Singh, V.R., Singh, H.R. (2008). Reduction of artefacts in 12-channel ECG signals using FastICA algorithm. Journal of Scientificand Industrial Research, 67, 43-48.
 Palaniappan, R. (2010). Biological Signal Analysis. Ventus Publishing.
 Patil, D., Das, N., Routray, A. (2011). Implementation of fast-ICA: A performance based comparison between floating points, and fixed point DSP platform. Measurement Science Review, 11 (4), 118-124.