Is It Significant? Guidelines for Reporting BCI Performance

  • Martin Billinger
  • Ian Daly
  • Vera Kaiser
  • Jing Jin
  • Brendan Z. Allison
  • Gernot R. Müller-Putz
  • Clemens Brunner
Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)


Recent growth in brain-computer interface (BCI) research has increased pressure to report improved performance. However, different research groups report performance in different ways. Hence, it is essential that evaluation procedures are valid and reported in sufficient detail. In this chapter we give an overview of available performance measures such as classification accuracy, cohen’s kappa, information transfer rate (ITR), and written symbol rate. We show how to distinguish results from chance level using confidence intervals for accuracy or kappa. Furthermore, we point out common pitfalls when moving from offline to online analysis and provide a guide on how to conduct statistical tests on (BCI) results.


Classification Accuracy Confusion Matrix Chance Level Event Related Desynchronization Information Transfer Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The views and the conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the corresponding funding agencies.

The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement 248320. In addition, the authors would like to acknowledge the following projects and funding sources:

\(\bullet \) Coupling Measures for BCIs, FWF project P 20848-N15

\(\bullet \) TOBI: Tools for Brain–Computer Interaction, EU project D-1709000020

\(\bullet \) Grant National Natural Science Foundation of China, grant no. 61074113.

We would like to express our gratitude towards the reviewers, who provided invaluable thorough and constructive feedback to improve the quality of this chapter.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Martin Billinger
    • 1
  • Ian Daly
    • 1
  • Vera Kaiser
    • 1
  • Jing Jin
    • 3
  • Brendan Z. Allison
    • 1
  • Gernot R. Müller-Putz
    • 1
  • Clemens Brunner
    • 1
    • 2
  1. 1.Institute for Knowledge DiscoveryGraz University of TechnologyGrazAustria
  2. 2.Swartz Center for Computational NeuroscienceINC, UCSDSan DiegoUSA
  3. 3.Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of EducationEast China University of Science and TechnologyShanghaiChina

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