A Critical Review on the Usage of Ensembles for BCI

In this chapter we review the employment of classifier ensembles for Brain Computer Interfaces (BCIs). We attain to the best of our knowledge the first review on the utilization of these kind of techniques in the BCI application field. This is a paradigm originated in the Machine Learning community, where a group of classifiers is applied to a data set and the obtained results are then integrated through a combining function. Ensembles have been recognized in recent general BCI surveys as an enormously interesting technique for classifying BCI data because of their capability to cope with the large variability of this kind of data. First we try to describe different design principles that can help users to quickly identify how to proceed when developing a new ensemble based BCI system. Moreover we make an extensive review of the most usual nomenclature. Our last goal is to summarize best practices, construction principles, and results obtained on different data sets for the sake of reference.

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