Abstract
Eye blinking, body parts movements, power line, and many other internal and external artifacts deteriorate the quality of EEG signal and the whole BCI system. There are some methods for removing artifacts or at least reducing their influence on the BCI system, however, they do not work efficiently when only few channels are used in the system and an automatic artifact elimination is required. The paper presents our approach to deal with artifacts in such a case by adding artificially generated signals to the set of originally recorded signals and to perform Independent Component Analysis on such an enlarged signal set. Our initial experiment, reported in this paper, shows that such an approach results in a better classification precision than when Independent Component Analysis is performed directly on the original signals set.
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Rejer, I., Górski, P. (2015). ICA for Detecting Artifacts in a Few Channel BCI. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_20
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DOI: https://doi.org/10.1007/978-3-319-24834-9_20
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