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Digital Signal Processing and Machine Learning

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Abstract

Any brain–computer interface (BCI) system must translate signals from the users brain into messages or commands (see Fig. 1). Many signal processing and machine learning techniques have been developed for this signal translation, and this chapter reviews the most common ones. Although these techniques are often illustrated using electroencephalography (EEG) signals in this chapter, they are also suitable for other brain signals.

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Acknowledgments

The authors are grateful to G. Townsend, B. Graimann, and G. Pfurtscheller for their permission to use Figures 7 and 8 in this chapter. The authors are also grateful to anonymous reviewers and the editors B. Graimann, G. Pfurtscheller, B. Allison of this book for their contributions to this chapter. Yuanqing Li’s work was partially supported by National Natural Science Foundation of China under Grant 60825306, Natural Science Foundation of Guangdong Province, China under Grant 9251064101000012. Kai Keng Ang and Cuntai Guan’s work were supported by the Science and Engineering Research Council of A*STAR (Agency for Science, Technology and Research), Singapore.

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Li, Y., Ang, K.K., Guan, C. (2009). Digital Signal Processing and Machine Learning. In: Graimann, B., Pfurtscheller, G., Allison, B. (eds) Brain-Computer Interfaces. The Frontiers Collection. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02091-9_17

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  • DOI: https://doi.org/10.1007/978-3-642-02091-9_17

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