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Multifrequency Analysis of Brain-Computer Interfaces

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Recent Progress in Brain and Cognitive Engineering

Part of the book series: Trends in Augmentation of Human Performance ((TAHP,volume 5))

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Abstract

Modern brain computer interfaces (BCI) rely on an extensive use of machine learning and signal processing techniques. This review will focus on an important prerequisite, namely spectral preprocessing. In particular, the optimal usage of multiple frequency features for BCI is discussed in general along with the commonly employed tricks for frequency choice. This is linked to the underlying physiology. Finally, applications of the multifrequency framework are given: (a) to BCI in general and (b) for analysing the BCI illiterates phenomenon.

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Notes

  1. 1.

    These authors contributed equally.

  2. 2.

    Event-related desynchronization/Event-related synchronization.

  3. 3.

    Basically, any variants of the standard CSP [8, 16, 43, 44] can be used for this.

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Acknowledgements

This work was supported by the Brain Korea 21 Plus Program as well as the SGER Grant 2014055911 through the National Research Foundation of Korea funded by the Ministry of Education. This publication only reflects the authors views. Funding agencies are not liable for any use that may be made of the information contained herein. The authors acknowledge the use of some text from the prior publications [45, 46] and thank their co-authors for allowing them to use materials from prior joint publications.

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    Authors and Affiliations

    1. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea

      Siamac Fazli, Heung-Il Suk & Seong-Whan Lee

    2. Machine Learning Group, Berlin Institute of Technology, Berlin, Germany

      Klaus-Robert Müller

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    1. Siamac Fazli
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    2. Heung-Il Suk
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    Correspondence to Siamac Fazli .

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    Editors and Affiliations

    1. Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea (Republic of)

      Seong-Whan Lee

    2. Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Tübingen, Germany

      Heinrich H. Bülthoff

    3. Machine Learning Group, Berlin Institute of Technology, Berlin, Germany

      Klaus-Robert Müller

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    Fazli, S., Suk, HI., Lee, SW., Müller, KR. (2015). Multifrequency Analysis of Brain-Computer Interfaces. In: Lee, SW., Bülthoff, H., Müller, KR. (eds) Recent Progress in Brain and Cognitive Engineering. Trends in Augmentation of Human Performance, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7239-6_4

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