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Multifrequency Analysis of Brain-Computer Interfaces

  • Siamac FazliEmail author
  • Heung-Il Suk
  • Seong-Whan Lee
  • Klaus-Robert Müller
Part of the Trends in Augmentation of Human Performance book series (TAHP, volume 5)

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.

Keywords

BCI EEG Spatio-temporal filtering Filter bank Bayesian framework BCI illiteracy 

Notes

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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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Siamac Fazli
    • 1
    Email author
  • Heung-Il Suk
    • 1
  • Seong-Whan Lee
    • 1
  • Klaus-Robert Müller
    • 2
  1. 1.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
  2. 2.Machine Learning GroupBerlin Institute of TechnologyBerlinGermany

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