  There are numerous possibilities and motivations for an adaptive BCI, which
may not be easy to clarify and organize for a newcomer to the field. To our
knowledge, there has not been any work done in classifying the literature on
adaptive BCI in a comprehensive and structured way. We propose a conceptual
framework, a taxonomy of adaptive BCI methods which encompasses most important
approaches to fit them in such a way that a reader can clearly visualize which
elements are being adapted and for what reason. In the interest of having a
clear review of existing adaptive BCIs, this framework considers adaptation
approaches for both the user and the machine, i.e., using instructional design
observations as well as the usual machine learning techniques. This framework
not only provides a coherent review of such extensive literature but also
enables the reader to perceive gaps and flaws in the current BCI systems, which
would hopefully bring novel solutions for an overall improvement.
