Abstract
In recent years, brain-computer interface (BCI) technology has emerged very rapidly. Brain-computer interfaces (BCIs) bring us a new communication interface technology which can translate brain activities into control signals of devices like computers, robots. The preprocessing of electroencephalographic (EEG) signal and translation algorithms play an important role in EEG-based BCIs. In this study, we employed an independent component analysis (ICA)-based preprocessing method and a committee machine-based translation algorithm for the offline analysis of a cursor control experiment. The results show that ICA is an efficient preprocessing method and the committee machine is a good choice for translation algorithm.
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References
Li, Y., Wang, J., Zurada, J.M.: Blind Extraction of Singularly Mixed Sources Signals. IEEE Trans. On Neural Networks 11, 1413–(2000)
Li, Y., Wang, J.: Sequential Blind Extraction of Linearly Mixed Sources. IEEE Trans. on Signal Processing 50, 997–1006 (2002)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley, New York (2002)
Makeig, S., Bell, A.J., Jung, T.-P., Sejnowski, T.J.: Independent Component Analysis of Electroencephalographic Data. Adv Neural Info Processing Systems 8, 145–151 (1996)
Comon, P.: Independent Component Analysis. A New Concept Signal Procesing 36, 287–314 (1994)
Bell, A.J., Sejnowski, T.J.: An Information-maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)
Amari, S., Chichocki, A., Yang, H.H.: A New Learning Algorithm for Blind Signal Separation. Advances in Neural Information Processing 8, 757–763 (1996)
Choi, S., Chichocki, A., Amari, S.: Flexible Independent Component Analysis. In: Proc. of the 1998 IEEE Workshop on NNSP, pp. 83–92 (1998)
Ramoser, H., Gerking, J.M., Pfurtscheller, G.: Optimal Spatial Filtering of Single Trial EEG during Imagined Hand Movement. IEEE Trans. Rehab. Eng. 8, 441–446 (2000)
McFarland, D.J., McCane, L.M., David, S.V., Wolpaw, J.R.: Spatial Filter Selection for EEG-based Communication. Electroenc. Clin. Neurophys. 103, 386–394 (1997)
Amari, S., Murata, N., Müller, K.-R., Finke, M., Yang, H.: Statistical Theory of Overtraining-Is Cross-Validation Asymptotically Effective. In: Advances in Neural Information Processing Systems, vol. 8, pp. 176–182. MIT Press, Cambridge (1996)
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Qin, J., Li, Y., Cichocki, A. (2005). ICA and Committee Machine-Based Algorithm for Cursor Control in a BCI System. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_156
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DOI: https://doi.org/10.1007/11427391_156
Publisher Name: Springer, Berlin, Heidelberg
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