On-line Eye Blink Suppression from EEG Signals Using Adaptive Independent Component Analysis for Brain Computer Interfacing
Subject Areas : electrical and computer engineeringF. Shayegh 1 , A. Erfanian 2 *
1 - University of Science and Technology
2 - University of Science and Technology
Keywords: Independent component analysiseye blink EEGneural networkbrain computer interface,
Abstract :
For several years, many efforts have been done to use the electro-encephalogram (EEG) as a new communication channel between human brain and computer. This new communication channel is called EEG-based brain-computer interface (BCI). The aim of brain-computer interface (BCI) research is to establish a new communication channel that directly translates brain activities into sequences of control commands for an output device such as a computer application or a neuroprosthesis. The major advantage of EEG-based BCI is that no physical movement is required. The motor imagery is the essential part of the most EEG-based communication systems. One of the major problems in developing a real-time Brain Computer Interface (BCI) is the eye blink artifact suppression. Recently, a more effective method has been introduced for removing a wide variety of artifacts from multi-channel EEG signals based on blind source separation by Independent Component Analysis (ICA). However, the method requires visual inspection of ICA components and manual classification of the interference components. This can be time-consuming and is not desirable for real-time artifact suppression. Moreover, the real-time application of this method for artifact rejection has not been considered so far. In this paper, various ICA methods with adaptive learning algorithm are presented and evaluated by computer simulation. The results from real-data demonstrate that the proposed scheme removes perfectly eye blink artifacts from the contaminated EEG signals and is suitable for use during on-line EEG monitoring and EEG-based brain computer interface.
[1] T. P. Jung, et al., "Removing electroencephalograohic artifacts by blind source separation," Psycophysiology, vol. 37, pp. 163-178, 2000.
[2] J. C. Woestenburg, M. N. Verbaten, and J. L. Slangen, "The remaoval of the eye-movement artifact from the EEG by regression analysis in the frequency domain," Biol. Psych., vol. 16, pp. 127–147, no. 1-2, Feb/Mar. 1993.
[3] P. He, G. Wilson, and C. Russel, "Removal of ocular artifacts from electro-encephalogram by adaptive filtering," Med. & Biol. Eng. & Compu.t, vol. 42, no. 3, pp. 407-412, 2004.
[4] P. K. Sadasivan and D. N. Dutt, "Development of newton-type adaptive algorithm for minimization of EOG artifacts from noisy EEG signals," Signal Processing, vol. 62, no. 2, pp. 173-186, Nov. 1997.
[5] A. Erfanian and B. Mahmoudi, "Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain–computer interface," Med. Biol. Compt., vol. 43, no. 2, pp. 296-305, 2005.
[6] A. Delorme, T.J. Sejnowski, and S. Makeig, "Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis," NeuroImage, vol. 34, no. 4, pp. 1443-49, 15 Feb. 2007.
[7] T.-P. Jung, et al., "Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects," Clin. Neurophysiol., vol. 111, no. 10, pp. 1745-1758, 2000.
[8] A. Hyvärinen and E. Oja, "Independent component analysis: algorithms and applications," Neural Networks, vol. 13, no. 4-5, pp. 411–430, 2000.
[9] S. Cruces, L. Castedo, and A. Cichocki, "Robust blind source separation algorithms using cumulants," Neurocomputing, vol. 49, pp. 87-118, no. 1-4, Dec. 2002.
[10] A. J. Bell and T. J. Sejnowski, "An information maximization approach to blind separation and blind deconvolution," Neural Computation, vol. 7, no. 6, pp. 1004-1034, Nov. 1995.
[11] T. -W. Lee, M. Girolami, and T. J. Sejnowski, "Independent component analysis using an Extended Infomax algorithm for mixed subgaussian and supergaussian sources," Neural Computation, vol. 11, no. 2, pp. 417-441, 15 Feb. 1999.
[12] A. Hyvärinen, J. Karhunen, and E. Oja, Independent Analysis, Wiely, 2001.
[13] J. Karhunen, E. Oja, L. Wang, R. Vigário, and J. Joutsensalo; "A class of neural networks for independent component analysis," IEEE Trans. on Neural Networks, vol. 8, no. 3, pp. 486-504, May 1997.
[14] A. Cichocki, R. Unbehauen, "Robust neural networks with on-line learning for blind identification and blind separation of sources," IEEE Trans. on Circuits and Systems-I: Fundamental Theory and Applications, vol. 43, no. 11, pp. 894-906, Nov. 2000.
[15] J. -F. Cardoso and B. H. Laheld, "Equivariant adaptive source separation," IEEE Trans. on Signal Processing, vol. 44, no. 12, pp. 3017-3030, Dec. 1996.
[16] S. Choi, A. Cichocki, and S. Amari, "Flexible independent component analysis," J. of VLSI Signal Processing, vol. 26, pp. 25-38, 2000.
[17] S. C. Douglas and A. Cichocki, "Neural networks for blind decorrelation of signals," IEEE Trans. on Signal Processing, vol. 45, no. 11, pp. 2829-2842, Nov. 1997
[18] J. Karhunen and J. Joutsensalo, "Representation and separation of signals using nonlinear PCA type learning," Neural Networks, vol. 7, no. 1, pp. 113-127, 1994.
[19] S. C-Alvarez, A. Cichocki, and L. C-Ribas, "An iterative inversion approach to blind source separation," IEEE Trans. on Neural Networks, vol. 11, no. 6, pp. 1423-1437, Nov. 2000.
[20] A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, New York, Wiley, 2001.
[21] C. Jutten and J. Herault, "Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture," Signal Processing, vol. 24, no. 1, pp. 1-10, Jul. 1991.