Abstract
Our goal is to develop a novel BCI based on an eye movements system employing EEG signals on-line. Most of the analysis on EEG signals has been performed using ensemble averaging approaches. However,It is suitable to analyze raw EEG signals in signal processing methods for BCI.
In order to process raw EEG signals, we used independent component analysis(ICA). However, we do not know which ICA algorithms have good performance. It is important to check which ICA algorithms have good performance to develop BCIs. Previous paper presented extraction rate of saccade-related EEG signals by five ICA algorithms and eight window size.
However, three ICA algorithms, the FastICA, the NG-FICA and the JADE algorithms, are based on 4th order statistic and AMUSE algorithm has an improved algorithm named SOBI.Therefore, we must re-select ICA algorithms.
In this paper, we add new algorithms; the SOBI and the MILCA. The SOBI is an improved algorithm based on the AMUSE and uses at least two covariance matrices at different time steps. The MILCA use the independency based on mutual information. Using the Fast ICA, the JADE, the AMUSE, the SOBI, and the MILCA, we extract saccade-related EEG signals and check extracting rates.
Secondly, in order to get more robustness against EOG noise, we use improved FastICA with reference signals and check extracting rates.
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Funase, A., Yagi, T., Kuno, Y., Uchikawa, Y.: A study on electro-encephalo-gram (EEG) in eye movement. Studies in Applied Electromagnetics and Mechanics 18, 709–712 (2000)
Cichocki, A., Amari, S.: Adaptive blind signal and image processing. Wiley, Chichester (2002)
Funase, A., Hashimoto, T., Yagi, T., Barros, A.K., Cichocki, A., Takumi, I.: Research for estimating direction of saccadic eye movements by single trial processing. In: Proc. of 29th Annual International Conference of the IEEE EMBS, pp. 4723–4726 (2007)
Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation (9), 1483–1492 (1997)
Choi, S., Cichocki, A., Amari, S.: Flexible independent component analysis. Journal of VLSI Signal Processing 26(1), 25–38 (2000)
Tong, L., Soon, V., et al.: Indeterminacy and indentifiability of blind indentification. IEEE Trans. CAS 38, 499–509 (1991)
Cardoso, J.-F., Souloumiac, A.: Blind beam-forming for non Gaussian signals. IEE Proceedings-F 140, 362–370 (1993)
Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., Moulines, E.: Second-order blind separation of temporally correlated sources. In: Proc. Int. Conf. on Digital Sig. Proc. (Cyprus), pp. 346–351 (1993)
Stogbauer, H., Kraskov, A., Astakhov, S.A., Grassberger, P.: Least Dependent Component Analysis Based on Mutual Information. Phys. Rev. E 70 (6), 066123 (2004)
Barros, A.K., Vigà rio, R., Jousmäki, V., Ohnishi, N.: Extraction of event-related signal form multi-channel bioelectrical measurements. IEEE Transaction on Biomedical Engineering 47(5), 61–65 (2001)
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Funase, A., Mouri, M., Cichocki, A., Takumi, I. (2009). Suitable ICA Algorithm for Extracting Saccade-Related EEG Signals. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_46
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DOI: https://doi.org/10.1007/978-3-642-10677-4_46
Publisher Name: Springer, Berlin, Heidelberg
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